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Tuesday, October 6
 

00:00 UTC

S101 - Opening Session
Tuesday October 6, 2026 00:00 - 00:30 UTC
Zoom Room #1
This session will welcome attendees, discuss the history of the IDWSDS Conference, and you’ll hear from the representatives from the Caucus for Women in Statistics & Data Science (CWS), International Statistics Institute (ISI) and other professional associations. They will share how their organizations help their members create global connections. We will also share technical information about how to access key features of the Sched App and our Zoom rooms.
Organizers
avatar for IDWSDS Program Committee
Tuesday October 6, 2026 00:00 - 00:30 UTC
Zoom Room #1
  Opening Session
  • Session ID 101

00:30 UTC

S102 - Gini-Weighted Risk Control in Adaptive Generative Augmentation
Tuesday October 6, 2026 00:30 - 01:00 UTC
Zoom Room #1
Generative data augmentation is widely used to address class imbalance by enriching minority classes with synthetic samples. Existing approaches typically employ a fixed augmentation strength across all classes, ignoring differences in class imbalance, data structure, and generative quality. We propose an adaptive augmentation framework that determines class-specific augmentation strengths using both class proportions and structural information measured through Gini correlation. This strategy allocates more synthetic data to underrepresented and weakly structured classes while limiting augmentation for well-represented classes.

We develop a theoretical framework showing that the resulting excess risk is controlled by a weighted combination of class-conditional Wasserstein discrepancies and Gini-based structural factors. We further establish consistency results and demonstrate that adaptive augmentation provides tighter control of risk distortion than fixed augmentation schemes.

Experiments on imbalanced classification datasets show consistent improvements in minority-class recall and macro-F1 performance, while empirical results closely match theoretical predictions. The proposed framework provides a principled, structure-aware foundation for generative data augmentation.
Speakers
avatar for Chathurika Abeykoon

Chathurika Abeykoon

Assistant professor of Mathematics and Statistics, Rhodes College
Chathurika Abeykoon is an Assistant Professor of Statistics at Rhodes College, Memphis, TN. Dr. Abeykoon received her Ph.D. in Mathematics with a concentration in Statistics from the University of Mississippi in 2023. Her research lies at the intersection of Mathematics, Statistics... Read More →
Organizers
avatar for Chathurika Abeykoon

Chathurika Abeykoon

Assistant professor of Mathematics and Statistics, Rhodes College
Chathurika Abeykoon is an Assistant Professor of Statistics at Rhodes College, Memphis, TN. Dr. Abeykoon received her Ph.D. in Mathematics with a concentration in Statistics from the University of Mississippi in 2023. Her research lies at the intersection of Mathematics, Statistics... Read More →
Tuesday October 6, 2026 00:30 - 01:00 UTC
Zoom Room #1
  Plenary Talk, Technical Statistical Session

00:30 UTC

S201- Vision-Language Models and the Harms of Covert Sexualization
Tuesday October 6, 2026 00:30 - 01:00 UTC
Zoom Room #2
Vision-language models (VLMs) increasingly mediate how bodies are described, moderated, and rendered in online spaces through content moderation, AI-generated images and videos, and descriptions of real bodies. Prior work establishes that VLMs sexually objectify bodies that are partially clothed more than fully clothed ones, and that plus-size bodies — particularly those belonging to women and AFAB persons — are disproportionately censored on social media platforms for being “inappropriate.” However, scholars lack the tools to detect when a model *objectifies* a body, rather than merely describing it, and whether this behavior differs across body *shapes*, rather than just body sizes. This talk describes a study in which we investigate these phenomena using swimwear try-on images matched across body morphology, comparing high-contrast silhouettes (i.e., a smaller waist relative to hips and bust) against lower-contrast silhouettes under identical prompting conditions. We ask whether VLMs describe these bodies differently despite equivalent context and whether unwanted objectification or descriptive drift disproportionately burdens those with high-contrast bodies, with a focus on covert (i.e., safety guardrail compliant) sexualization. By making the harms of these phenomena visible and measurable, this work gives researchers and auditors the ability to hold systems accountable when they objectify or censor women and AFAB persons based on their appearance.
Speakers
avatar for Maimuna Majumder

Maimuna Majumder

Harvard Medical School & Boston Children's Hospital
Dr. Maimuna (Maia) Majumder (she/they), MPH, PhD (MIT '18) is an Assistant Professor and Inaugural Peter Szolovits Distinguished Scholar in the Computational Health Informatics Program at Harvard Medical School and Boston Children’s Hospital. She is a computational epidemiologist... Read More →
Organizers
avatar for Maimuna Majumder

Maimuna Majumder

Harvard Medical School & Boston Children's Hospital
Dr. Maimuna (Maia) Majumder (she/they), MPH, PhD (MIT '18) is an Assistant Professor and Inaugural Peter Szolovits Distinguished Scholar in the Computational Health Informatics Program at Harvard Medical School and Boston Children’s Hospital. She is a computational epidemiologist... Read More →
Tuesday October 6, 2026 00:30 - 01:00 UTC
Zoom Room #2
  Plenary Talk, Technical Statistical Session

00:30 UTC

S301 - Why and When Targeted Validation Sampling Offers Statistical Efficiency Gains: A Case Study on Healthy Food Access and Disease Outcomes
Tuesday October 6, 2026 00:30 - 01:00 UTC
Zoom Room #3
Quantifying neighborhood food environments and understanding their relationships with residents’ health is a public health priority. Using simple, error-prone food access metrics (like the shortest straight-line routes to healthy food stores) introduces measurement error and biases downstream statistical models, but measuring the more-accurate, map-based ones (like shortest driving routes) for entire studies is often implausible. Fortunately, adopting a two-phase design can harness the best of both metrics by combining the error-prone access measures for the entire study and the more-accurate ones for a chosen subset in a partial validation study. This validated subset can be strategically chosen to not only reduce bias but further improve efficiency when modeling relationships between health and the food environment. Technically, any information that is fully available for all neighborhoods can guide the validation sampling strategy. One such promising design paired stratification with Neyman allocation and sampled based on the influence function within each stratum. Using simulations and data for the Piedmont Triad Region of North Carolina, various validation sampling designs were evaluated to quantify the associations of diabetes count and obesity prevalence with neighborhood-level access to healthy foods, fitting two separate Poisson regression models, one for each outcome. We assess which design suits each model, and whether any is robust across settings.
Speakers
avatar for L. Ishara Wijayaratne

L. Ishara Wijayaratne

Department of Statistics, Wake Forest University
I am a graduate student in Statistics at Wake Forest University. Originally from Sri Lanka, I am keen on giving back to the community that has helped change my life so considerably. I am passionate about biostatistics, and my abstract submission addresses a pressing public health... Read More →
Organizers
avatar for L. Ishara Wijayaratne

L. Ishara Wijayaratne

Department of Statistics, Wake Forest University
I am a graduate student in Statistics at Wake Forest University. Originally from Sri Lanka, I am keen on giving back to the community that has helped change my life so considerably. I am passionate about biostatistics, and my abstract submission addresses a pressing public health... Read More →
Tuesday October 6, 2026 00:30 - 01:00 UTC
Zoom Room #3
  Plenary Talk, Technical Statistical Session

01:00 UTC

K1 - A Statistician in the Loop: Building a Career in Trustworthy AI
Tuesday October 6, 2026 01:00 - 02:00 UTC
Zoom Room #1
Statistics provides tools for answering questions that are central to building trustworthy AI: What works? What fails? How certain are we? In this talk, I will discuss my path from studying statistics to conducting research as a data scientist and now working full time in AI safety. I will share examples of how statistical thinking can support this work, including deciding what to measure, uncovering failure modes, quantifying uncertainty, and connecting evidence to real decisions. I hope to show that many paths lead into this field and that statisticians and data scientists have important skills to bring to the challenge of building trustworthy AI.
Speakers
avatar for Emily Hadley

Emily Hadley

Quantitative Threat Forecasting Analyst, OpenAI
Emily Hadley is a Quantitative Threat Forecasting Analyst at OpenAI, where she works to identify, understand, and forecast risks related to AI. Previously, she was a Security Researcher on the Microsoft AI Red Team and a Senior Research Data Scientist at RTI International. Emily holds... Read More →
Organizers
avatar for IDWSDS Program Committee
Tuesday October 6, 2026 01:00 - 02:00 UTC
Zoom Room #1
  Keynote Session

02:00 UTC

S103- Decoding Health: AI and Big Data at the Frontier of Precision Medicine
Tuesday October 6, 2026 02:00 - 03:00 UTC
Zoom Room #1
This session explores how artificial intelligence and data science are driving the next generation of precision medicine, drawing on large-scale biobanks, electronic health records (EHRs), and genomic data from both academia and industry. Dr. Marie Loh (Nanyang Technological University / Genome Institute of Singapore) will discuss translational opportunities from HELIOS-SG100K, a multi-ethnic Asian population cohort with rich phenotypic and molecular data, with examples in cardiometabolic disease and atopic dermatitis. Ms. Chih-Ting Yang (Vanderbilt University) will use the All of Us Research Program to show how biobank data can be translated into longitudinal disease staging, illustrated through a study of cardiovascular-kidney-metabolic syndrome. Dr. Sarah Lotspeich (Wake Forest University) will present an LLM-enhanced, ICD-10-based algorithm for recovering missing EHR data, demonstrating accuracy comparable to expert chart review at scale. Rounding out the session, Dr. Christine Hsiung will bring an industry perspective from the genetics sector, sharing applied research insights from Taiwan Biobank genomic data and translational pipelines. Together, these talks span academic and industry approaches, genomic profiling, longitudinal disease modeling, and AI-driven data quality methods, illustrating how data science is reshaping precision medicine across diverse populations and settings.
Speakers
avatar for Sarah Lotspeich

Sarah Lotspeich

Assistant Professor of Statistics, Wake Forest University
Sarah Lotspeich is an Assistant Professor in Statistical Sciences at Wake Forest University. She co-leads the Spatial and Environmental Statistics in Health (SESH) Lab at Wake Forest and the Missing and INcomplete Data (MIND) Lab at UNC Chapel Hill, and is enthusiastic about mentoring... Read More →
avatar for Marie Loh

Marie Loh

Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
Marie Loh is an Assistant Professor in the Lee Kong Chian School of Medicine at Nanyang Technological University, Senior Research Scientist at the Genome Institute of Singapore and Honorary Senior Lecturer at Imperial College London. Asst Prof Loh is a molecular epidemiologist with... Read More →
avatar for Chih-Ting Yang

Chih-Ting Yang

Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
Chih-Ting Yang is a PhD candidate in Biostatistics at Vanderbilt University. Her research focuses on developing and applying statistical methods to complex biomedical data, including microbiome and single-cell omics data, electronic health records, and biobank-linked clinical dat... Read More →
avatar for Chiani Hsiung

Chiani Hsiung

Genetics Generation Advancement Corp.(GGA Corp.)
Dr. Chia-Ni Hsiung (Christine Hsiung) is a Taiwan-based researcher in genomic epidemiology and precision medicine. She holds an MPH from the National Defense Medical Center and a Ph.D. in Precision Medicine from National Tsing Hua University (2026). She currently serves as Senior... Read More →
Chairs/Hosts
YL
Organizers
YL
S103 Abstracts pdf
Tuesday October 6, 2026 02:00 - 03:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session

03:00 UTC

S104 - Breaking barriers
Tuesday October 6, 2026 03:00 - 04:00 UTC
Zoom Room #1
An Invited Session Proposal from Statistics Society of Australia (SSA) Women in Statistics Network Supported by the The International Statistical Institute (ISI) Committee on Women in Statistics

Session Title: Breaking barriers

Session Organiser: Alysha De Livera

Session Chair: Ayse Aysin Bilgin

The progression of the talks:

curiosity → identity → contribution → legacy
aspiration → exploration → contribution → impact

Speakers:
1. Jayamini Liyanage, [email protected], La Trobe University, Australia, (PhD Student)
2. Melissa Middleton, PhD, GStat, [email protected], Murdoch Children’s Research Institute, Australia, (Early Career)
3. Alysha De Livera, PhD, AStat, [email protected], Latrobe University, Australia, (Mid Career)
4. Ayse Aysin Bilgin, PhD, [email protected], Macquarie University, Australia Professor (Late Career)
Speakers
avatar for Alysha De Livera

Alysha De Livera

La Trobe University
Dr Alysha De Livera is Co-Chair of the newly established Women in Statistics and Data Science Special Interest Group of the Statistical Society of Australia, and previously served as Co-Chair of its Biostatistics and Bioinformatics Committee. She is an academic in Statistics in the... Read More →
avatar for Ayse Aysin Bilgin

Ayse Aysin Bilgin

Macquarie University, Australia
Honorary Prof Ayse Aysin Bilgin is a Vice President of International Statistical Institute (2025-2029), co-chair of Statistics Education Section of Statistical Society of Australia and was the President of the International Association for Statistical Education (IASE).
She has held a range of academic leadership roles, contributing to curriculum design, program development, and institutional strategy in teaching and learning at Macquarie University in Australia. Bilgin is recognised for her contributions to the scholarship of teaching and learni... Read More →
avatar for Jayamini Liyanage

Jayamini Liyanage

La Trobe University
Jayamini Liyanage is a final-year Biostatistics PhD student at La Trobe University, Melbourne, where her research focuses on developing multivariate meta-analysis methods for analysing high-dimensional biological data. She has published statistical methods and developed an R package... Read More →
avatar for Melissa Middleton

Melissa Middleton

Murdoch Children's Research Institute
Dr Melissa Middleton is an early-career biostatistician at the Murdoch Children’s Research Institute in Melbourne. Her work methodological work focuses on missing data methods and adaptive platform trial design, alongside her collaborative work in clinical trials and observational... Read More →
Chairs/Hosts
avatar for Ayse Aysin Bilgin

Ayse Aysin Bilgin

Macquarie University, Australia
Honorary Prof Ayse Aysin Bilgin is a Vice President of International Statistical Institute (2025-2029), co-chair of Statistics Education Section of Statistical Society of Australia and was the President of the International Association for Statistical Education (IASE).
She has held a range of academic leadership roles, contributing to curriculum design, program development, and institutional strategy in teaching and learning at Macquarie University in Australia. Bilgin is recognised for her contributions to the scholarship of teaching and learni... Read More →
Organizers
avatar for Alysha De Livera

Alysha De Livera

La Trobe University
Dr Alysha De Livera is Co-Chair of the newly established Women in Statistics and Data Science Special Interest Group of the Statistical Society of Australia, and previously served as Co-Chair of its Biostatistics and Bioinformatics Committee. She is an academic in Statistics in the... Read More →
S104 Abstracts pdf
Tuesday October 6, 2026 03:00 - 04:00 UTC
Zoom Room #1
  Invited Session, Career or Professional Development Session

04:00 UTC

S203 - AI and Data Science 1
Tuesday October 6, 2026 04:00 - 05:00 UTC
Zoom Room #2
Who Gets Heard? Investigating Accent and Gender Disparities in Automatic Speech Recognition | COMPARATIVE ANALYSIS OF FEATURE EXTRACTION APPROACHES IN A SINGLE-CHANNEL MULTISENSORY SETTING | Bridging Data Silos: A Two-Step Unsupervised Approach to Record Linkage | Mining the Foundations of Truth: Extracting Structured Knowledge Graphs from Unstructured Classical Texts
Speakers
avatar for Fouziah Md Yassin

Fouziah Md Yassin

Universiti Malaysia Sabah
Fouziah Md. Yassin is a lecturer in the field of Electronics and Communication Engineering at Universiti Malaysia Sabah (UMS). She obtained her Master of Engineering in Electronic and Communication Engineering from the University of York, United Kingdom. Her research interests include... Read More →
avatar for Tisha Prasad

Tisha Prasad

STEM4Change
Tisha Prasad is the co-founder and co-president of STEM4Change, a nonprofit organization dedicated to expanding access to STEM education by teaching students foundational concepts like coding. She also serves as Head of Web Development at VolunteerConnect, where she leads the development... Read More →
avatar for Zaturrawiah Ali Omar

Zaturrawiah Ali Omar

Universiti Malaysia Sabah
Zaturrawiah Ali Omar is a lecturer at Universiti Malaysia Sabah (UMS) and a PhD candidate at Universiti Kebangsaan Malaysia (UKM), where her research focuses on record linkage, machine learning, and road traffic injury surveillance in Malaysia. Her work centres on using the Unsupervised... Read More →
avatar for Norhafiza Hamzah

Norhafiza Hamzah

UMS
Norhafiza is a lecturer and researcher at Universiti Malaysia Sabah, developing knowledge graph and machine learning frameworks for fact-checking religious misinformation on social media. She teaches courses in Artificial Intelligence, Machine Learning, and Computer Science courses... Read More →
Organizers
avatar for IDWSDS Program Committee
S203 Abstracts pdf
Tuesday October 6, 2026 04:00 - 05:00 UTC
Zoom Room #2
  Contributed Session, Technical Statistical Session, Other

04:00 UTC

S105 - Towards Inclusive Medical AI: Mitigating Data Bias and Fostering Equity
Tuesday October 6, 2026 04:00 - 05:00 UTC
Zoom Room #1
As artificial intelligence becomes deeply integrated into healthcare, addressing inherent biases and ensuring equitable outcomes is paramount. This session explores multidisciplinary strategies for developing inclusive and fair medical AI systems. The panel brings together four experts to discuss critical challenges and actionable solutions. Dr. Heajin Kim (Chair) will highlight the importance of reflecting sex and gender characteristics in medical AI to foster equity. Researcher Jeongyeon Kim will provide a critical analysis of data bias issues within Korea's AI Hub. Dr. Yuseong Chu will review current research trends and methodologies for achieving algorithmic fairness in healthcare. Finally, Dr. Wonjung Park will introduce innovative approaches utilizing synthetic data generation to address dataset imbalances. Together, this session offers a comprehensive roadmap towards trustworthy and inclusive medical AI.
Speakers
avatar for Jeongyeon Kim

Jeongyeon Kim

Researcher
Jeongyeon Kim is a Researcher at the Center for Gendered Innovations in Science and Technology Research (GISTeR). She earned her bachelor’s degree with a double major in Statistics and Information Technology. Her current work focuses on analyzing bias and representativeness in healthcare... Read More →
avatar for Yusung Chu

Yusung Chu

Yonsei University, Postdoctoral Researcher
Dr. Yusung Chu received his B.S. and Ph.D. degrees in Biomedical Engineering from Yonsei University. He is currently a Postdoctoral Researcher in the Department of Precision Medicine at Yonsei University Wonju College of Medicine. His research focuses on medical artificial intelligence... Read More →
avatar for Wonjung Park

Wonjung Park

KAIST
Dr. Wonjung Park is a Postdoctoral Researcher in the Computer Graphics and Visualization Lab at KAIST. She received her Ph.D. in Computer Science from KAIST in 2026. Her research specializes in NeuroImaging and Generative AI, with a particular focus on the biological trustworthiness... Read More →
Chairs/Hosts
HK
Organizers
HK
S105 Abstracts pdf
Tuesday October 6, 2026 04:00 - 05:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session

05:00 UTC

S106 - Statistical Methods and Applications 1
Tuesday October 6, 2026 05:00 - 06:00 UTC
Zoom Room #1
Refining a Two-Stage Pipeline for Gene-by-Environment Interaction Discovery: Random-Forest Screening Followed by SOIL-Based Prioritization | Mapping Inequality: Women Statisticians and the Settlement House Movement | Precision by Design: A Transferable Framework for Sampling Optimisation in Hierarchical Capture-Recapture|How Biostatistics Research Experience Enriches the Pre-Medical Journey
Speakers
avatar for Su Na Chin

Su Na Chin

Universiti Malaysia Sabah, Malaysia
Dr. Chin Su Na is a statistician and Lecturer in Statistics at the Faculty of Science and Technology, Universiti Malaysia Sabah, Malaysia. Her research focuses on developing statistical methodology for efficient sampling design and population estimation. She received her PhD in Mathematical... Read More →
avatar for Kakon Datta

Kakon Datta

University of Kentucky
Kakon Datta is a Ph.D. student in the Department of Statistics at the University of Kentucky. She completed her master’s degree at Miami University, Ohio, USA in 2024. Her research interests include high-dimensional data analysis, variable selection, sufficient variable selection... Read More →
avatar for Tisha Prasad

Tisha Prasad

STEM4Change
Tisha Prasad is the co-founder and co-president of STEM4Change, a nonprofit organization dedicated to expanding access to STEM education by teaching students foundational concepts like coding. She also serves as Head of Web Development at VolunteerConnect, where she leads the development... Read More →
avatar for Kevin Zou

Kevin Zou

University of Central Florida
Kevin Zou is an undergraduate student majoring in Health Sciences (B.S.) on the Pre-Clinical Track at the University of Central Florida (Class of 2029). He is an Undergraduate Researcher in Dr. Julia Soulakova's biostatistics research team, where his work explores public health disparities... Read More →
Organizers
avatar for IDWSDS Program Committee
S106 Abstracts pdf
Tuesday October 6, 2026 05:00 - 06:00 UTC
Zoom Room #1
  Contributed Session, Technical Statistical Session, History of Women Statisticians Session, Career or Professional Development Session

06:00 UTC

K2 - Bayes in Practice: A Bayesian Cancer Atlas
Tuesday October 6, 2026 06:00 - 07:00 UTC
Zoom Room #1
From its earliest beginnings, Bayesian statistics has been a synthesis of theory, methodology, computation and application. In this presentation, I will reflect on the development of the award-winning Australian Cancer Atlas (https://atlas.cancer.org.au/) and spotlight its Bayesian foundations. I will highlight some of the challenges and proposed solutions to modelling and visualisation of an awkward spatial geography, “filling in” missing covariates, and communicating uncertainty. I will also touch on new research directions inspired by the Atlas: new spatio-temporal models, spatial vulnerability indices, meta-analysis transfer learning, distributed AI and responsible data science. Importantly, these methodological discussions will be complemented by reflections on the impact of the work for patients, health practitioners, cancer support groups and government agencies. Bayesian statistics really can make a difference!
Selected References

Baade P, K Mengersen [2024] Building HOPE through the Australian Cancer Atlas Insight+ MJA 35  //insightplus.mja.com.au/2024/35

J Bon, A Bretherton, K Buchhorn, S Cramb, C Drovandi, C Hassan, A Jenner, H Mayfield, J. McGree, K Mengersen, A Price, R Salomone, E Santos-Fernandez, J Vercelloni & X Wang, [2023] Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics. Philosophical Transactions. Series A, Mathematical, physical, and engineering sciences, 381(2247), Article number: 20220156.

Bretherton A, Bon J, Warne D, Mengersen K, Drovandi C, [2026] A Principled Approach to Bayesian Transfer Learning, Bayesian Analysis. To appear.

Cramb SM, K Mengersen, and PD Baade. [2011] Developing the atlas of cancer in Queensland: methodological issues. International Journal of Health Geographics, 10, p 1-11, 2011

Goodwin S,  T Saunders, J Aitken, P Baade, U Chandrasiri, D Cook, S Cramb, E Duncan, S Kobakian, J. Roberts, K. Mengersen, [2024] Designing the Australian Cancer Atlas: visualizing geostatistical model uncertainty for multiple audiences.  JAMIA. Journal of the American Medical Informatics Association, //doi.org/10.1093/jamia/ocae212

Hassan, C [2024] Structured Models and Algorithms for Sensitive Data. PhD Thesis, Queensland University of Technology, Australia.

Hogg J, J Cameron, S Cramb, P Baade, K Mengersen [2024]  A Two‐stage Bayesian Small Area Estimation Approach for Proportions.  International Statistical Review. V92 I3 455482.

Hyland-Wood B, Snoswell A, Sandeep R, Chun O, Perrin D, Fielt E, Price A, Mengersen K (2024) Response to proposals paper on introducing mandatory guardrails for AI in highrisk settings. Analysis and Policy Observatory, 2024/10/4

Jahan F, Duncan E, Cramb S, Baade P, Mengersen K, [2020] Multivariate Bayesian metaanalysis: Joint modelling of multiple cancer types using summary statistics, International Journal of Health Geographics, 19 (1)

Leontyeva Y, Y Huang, S Cramb, J Cameron, P Baade, K Mengersen, et al. [2025] Bayesian Spatial Relative Survival Model to Estimate the Loss in Life Expectancy and Crude Probability of Death for Cancer Patients, Statistics in Medicine 44 (3–4), e10287

Price A, M Rigby, P Fiévez, K Mengersen [2025] A spatial vulnerability index for environmental health Ecological Indicators Vol 178, September 2025, 113793 
Speakers
avatar for Kerrie Mengersen
Organizers
avatar for IDWSDS Program Committee
Tuesday October 6, 2026 06:00 - 07:00 UTC
Zoom Room #1
  Keynote Session

07:00 UTC

S107 - Absorbing Markov Chain Parameter Estimation Under Data Scarcity: A Comparative Study of Analytical and Monte Carlo Methods in Neonatal Care
Tuesday October 6, 2026 07:00 - 07:30 UTC
Zoom Room #1
Neonatal mortality remains a major global public health challenge, with an estimated 6,200 newborns dying daily, mostly in settings where patient records are scarce. For data-scarce neonatal units, a key question arises: when transition data is limited, does it matter whether Analytical Estimation or Monte Carlo Simulation is used to model patient outcomes?

This study addresses that question using a neonatal dataset of 6,000 daily state transitions across 2,000 patients in Ghana. An absorbing Markov chain with four states: Hospital Admission, Neonatal Intensive Care Unit (NICU), Recovered, and Death was used. Both methods were evaluated across six data levels with 500 independent replications using bias, variance, standard deviation, and mean squared error.

Both methods perform similarly with adequate data and degrade equivalently under data scarcity because they share the same estimated transition matrix. Expected time to absorption is more sensitive to limited data than absorption probabilities, and 500 observed transitions emerge as the minimum reliable threshold. These findings provide an evidence-based data standard for resource-constrained healthcare systems.

Keywords: Absorbing Markov Chain, Monte Carlo Simulation, Analytical Estimation, Absorption Probability, Expected Time to Absorption, Sensitivity Analysis.

Authors: Emmanuella Frimpong, Dr. Irene Kafui Vorsah Amponsah, PhD (Visiting Lecturer at Ohio University)
Speakers
avatar for Emmanuella Frimpong

Emmanuella Frimpong

Miami University, Oxford, Ohio
Ms. Emmanuella Frimpong is a graduate of the African Institute for Mathematical Sciences (AIMS) Ghana, where she completed her Master of Science in Mathematics, undertaking research on the topic “Comparing Monte Carlo Simulation and Analytical Estimation Methods for Absorbing Markov... Read More →
Organizers
avatar for Emmanuella Frimpong

Emmanuella Frimpong

Miami University, Oxford, Ohio
Ms. Emmanuella Frimpong is a graduate of the African Institute for Mathematical Sciences (AIMS) Ghana, where she completed her Master of Science in Mathematics, undertaking research on the topic “Comparing Monte Carlo Simulation and Analytical Estimation Methods for Absorbing Markov... Read More →
Tuesday October 6, 2026 07:00 - 07:30 UTC
Zoom Room #1
  Plenary Talk, Technical Statistical Session

07:00 UTC

S204 - Creating Global Connections Through Early Statistical Leadership in Rare Disease Trials
Tuesday October 6, 2026 07:00 - 07:30 UTC
Zoom Room #2
In complex clinical research, collaboration begins well before database lock or final analysis. It begins when statisticians are involved early enough in protocol development to shape what is measurable, sustainable, and meaningful for patients. This presentation uses a rare pediatric dermatology trial as a case study to show how early statistical input can prevent avoidable missing data before the first participant is enrolled.

The original design was scientifically ambitious but operationally burdensome, with frequent in-clinic visits, narrow assessment windows, and substantial caregiver burden. Statistically, this created a foreseeable risk of informative missingness, loss to follow-up, and reduced interpretability in a small and vulnerable population. Early statistical leadership helped reframe the design around feasibility as well as rigor.

The talk will illustrate how collaboration among statisticians, clinicians, programmers, operations teams, and digital partners can reduce this risk. Examples include identifying burden-sensitive endpoints, anticipating missing-data pathways, and considering remote ePRO, image capture, or AI-supported skin assessment to replace selected in-person visits without compromising data quality.

The broader message is that statistics is not only an analysis discipline; it is also a design and connecting discipline.
Speakers
avatar for Shrutimita Pokhariyal

Shrutimita Pokhariyal

PHASTAR
Shrutimita Pokhariyal is an experienced biostatistics professional with over 14 years in clinical research, with expertise spanning multiple phases of clinical trials. Her work includes statistical strategy, study design, protocol and SAP development, and collaboration across cross-functional... Read More →
Chairs/Hosts
avatar for Shrutimita Pokhariyal

Shrutimita Pokhariyal

PHASTAR
Shrutimita Pokhariyal is an experienced biostatistics professional with over 14 years in clinical research, with expertise spanning multiple phases of clinical trials. Her work includes statistical strategy, study design, protocol and SAP development, and collaboration across cross-functional... Read More →
Organizers
avatar for Shrutimita Pokhariyal

Shrutimita Pokhariyal

PHASTAR
Shrutimita Pokhariyal is an experienced biostatistics professional with over 14 years in clinical research, with expertise spanning multiple phases of clinical trials. Her work includes statistical strategy, study design, protocol and SAP development, and collaboration across cross-functional... Read More →
Tuesday October 6, 2026 07:00 - 07:30 UTC
Zoom Room #2
  Plenary Talk, Career or Professional Development Session

07:00 UTC

S303 - AI and Data Science 2
Tuesday October 6, 2026 07:00 - 08:00 UTC
Zoom Room #3
Observation-Weight-Based Strategies for Robust Stochastic Gradient Boosting in Genomic Prediction | A journey of pivoting: From Local Research Experiences to Global Data Science Connections | Teaching Statistics in the Age of AI: Beyond Calculation in Health Sciences Education
Speakers
avatar for Beatriz Gil Comparado

Beatriz Gil Comparado

FCTNOVA Math
Beatriz Gil Comparado is pursuing a Master's degree in Mathematics and Applications – Specialization in Data and Decision Sciences at NOVA School of Science and Technology (NOVA FCT), Portugal. Her research focuses on statistical methodology and machine learning, with particular... Read More →
avatar for Cherlynn Dumbura

Cherlynn Dumbura

CeSHHAR
Cherlynn Dumbura is a Zimbabwean data scientist and health researcher working across climate-health, maternal and newborn health, statistics and research. She is a board member of Applied Malaria Modeling network and in the leadership of International Biometric Society, Zimbabwean... Read More →
avatar for Camila Martins

Camila Martins

Associate Professor
Camila Bertini Martins is an Associate Professor at the Federal University of São Paulo (UNIFESP), Brazil, where she teaches Biostatistics in health sciences programs. She holds a PhD in Statistics from the University of São Paulo (USP). Her research interests include meta-analysis... Read More →
Organizers
avatar for IDWSDS Program Committee
S302 Abstracts pdf
Tuesday October 6, 2026 07:00 - 08:00 UTC
Zoom Room #3
  Contributed Session, Technical Statistical Session, Career or Professional Development Session, Other

07:30 UTC

S108 - Explainable AI in Health Technology
Tuesday October 6, 2026 07:30 - 08:00 UTC
Zoom Room #1
The rapid deployment of machine learning in healthcare has exposed a fundamental tension between model performance and clinical utility. This talk addresses the urgent need for Explainability (XAI), moving beyond the "black box" paradigm to ensure that AI systems are not only accurate but also transparent, accountable, and trustworthy. In the high-stakes environment of medical diagnostics, understanding an algorithm’s pathway is essential for verifying clinical reliability and meeting the rigorous standards demanded by both clinicians and regulators.
A central theme of this talk is the transition from surface-level performance metrics to deep reproducibility and validation. We will examine how traditional accuracy scores can sometimes be deceptive and how to combat this, compare XAI with true interpretability, and tackle the challenge of the inheritance of bias. This session outlines proactive mitigation strategies, including rigorous data quality checking, subgroup analysis, and continuous fairness assessments throughout the model lifecycle.
Finally, the talk will navigate the evolving regulatory landscape, specifically the EU AI Act.
Speakers
avatar for Autumn Johnson

Autumn Johnson

University of Galway
My name is Autumn Johnson, and I’m a postdoctoral researcher in Statistics at the University of Galway. My postdoctoral work has focused on health technology advancements using complex statistical methods, machine learning, and AI. My PhD is from University College Cork, where I... Read More →
Organizers
avatar for Autumn Johnson

Autumn Johnson

University of Galway
My name is Autumn Johnson, and I’m a postdoctoral researcher in Statistics at the University of Galway. My postdoctoral work has focused on health technology advancements using complex statistical methods, machine learning, and AI. My PhD is from University College Cork, where I... Read More →
Tuesday October 6, 2026 07:30 - 08:00 UTC
Zoom Room #1
  Plenary Talk, Technical Statistical Session

08:00 UTC

S304 - Education and Leadership 1
Tuesday October 6, 2026 08:00 - 09:00 UTC
Zoom Room #3
A Bayesian satellite turbidity calibration algorithm framework with a focus on the Great Barrier Reef, Australia | A Statistical Framework for Educational Analytics Using Learning Management System Data: A Data-Driven Approach to Improving Student Learning | Implementing the ADAPT Model: Measuring Fidelity and Translating Research into Instructor Support | Implementation of Machine Learning Techniques for analyzing Autism Spectrum Disorder children through Art Emotion Extraction
Speakers
avatar for Nasim Sadra

Nasim Sadra

PhD Candidate, Massey University
I am a PhD candidate in New Zealand with a background in Civil Engineering. I later moved into remote sensing and data science, developing an interest in using these tools to better understand environmental processes. My current research focuses on satellite remote sensing and statistical... Read More →
avatar for Abinaya Nagamuthu

Abinaya Nagamuthu

Senior Lecturer
I am a Senior Lecturer in Statistics at IBSUniversity in Papua New Guinea and an Adjunct Lecturer at Southern Cross University with a strong academic background in statistics, business analytics, and econometrics. I hold an M.Phil. in Statistics and have completed a professional certification... Read More →
avatar for Laura Short

Laura Short

North Carolina State University
Laurie Short is a third-year PhD student in the Department of STEM Education at North Carolina State University. Her research focuses on college-level mathematics education, with a specific interest in the preparation of mathematics graduate teaching assistants. She has also researched... Read More →
avatar for Vijayalakshmi C

Vijayalakshmi C

Professor, Department of Statistics and Applied Mathematics,Central University of Tamil Nadu,Thiruvarur
Professor & Head in the Department of Statistics and Applied Mathematics, Central University of Tamil Nadu. The teaching milestone records 25 years and 2 years industry. Published 136 research articles in journals with high IF and 79 papers in conference proceedings. The Google scholar... Read More →
Organizers
avatar for IDWSDS Program Committee
S303 Abstracts pdf
Tuesday October 6, 2026 08:00 - 09:00 UTC
Zoom Room #3
  Contributed Session, Technical Statistical Session, Other

08:00 UTC

S109 - Advances in Statistical Methods for Complex and High-Dimensional Data
Tuesday October 6, 2026 08:00 - 09:00 UTC
Zoom Room #1
This session brings together recent advances in statistical methodology for analyzing complex, high-dimensional, and heterogeneous data arising in modern biomedical and genetic research. The presentations cover a broad range of topics, including variable screening for mixed-type and high-dimensional data, integration of mechanistic and statistical models for infectious disease prediction, semiparametric approaches for meta-analysis, and set-based association testing for functional genetic responses. Together, these talks highlight emerging statistical frameworks that improve flexibility, scalability, and interpretability in the analysis of complex data structures.
Speakers
avatar for Haeun Moon

Haeun Moon

Seoul National University
Haeun Moon is an assistant professor in the Department of Transdisciplinary Innovations and the Department of Statistics at the Seoul National University, South Korea. Before joining SNU, she was a postdoctoral researcher in the Department of Statistics and Data Science at Carnegie... Read More →
avatar for Saebom Jeon

Saebom Jeon

Sungshin Women’s University
Saebom Jeon is an associate professor in the School of Mathematics, Statistics and Data Science at Sungshin Women’s University and a research member of the Biomedical Mathematics Group at the Institute for Basic Science (IBS), Korea. She received her Ph.D. in Statistics from Korea... Read More →
avatar for Eunjee Lee

Eunjee Lee

Chungnam National University
Eunjee Lee is an Associate Professor in the Department of Information and Statistics at Chungnam National University. Her research focuses on functional data analysis, biomedical imaging, brain network analysis, and Bayesian methodology. She has developed statistical models for complex... Read More →
avatar for Sunyoung Shin

Sunyoung Shin

Pohang University of Science and Technology
Sunyoung Shin is an Associate Professor in Department of Mathematics at Pohang University of Science and Technology. Her research lies at the intersection of statistical learning and reinforcement learning, with a focus on developing scalable methods for high-dimensional data, particularly... Read More →
Chairs/Hosts
SY
Organizers
SY
S109 Abstracts pdf
Tuesday October 6, 2026 08:00 - 09:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session

08:00 UTC

S205 - Time-to-event data in biometry: Challenges, biases and perspectives
Tuesday October 6, 2026 08:00 - 09:00 UTC
Zoom Room #2
In applied medical statistics, researchers encounter a wide variety of data structures. Among these, time-to-event data play a central role including survival times, transitions between disease stages, and sequences of medical events or progressions of patients’ states over time.
A broad spectrum of analytical approaches is available, ranging from (semi-)parametric methods such as the Cox proportional hazards model to flexible spline-based and other advanced modeling strategies. Depending on the motivation we encounter unique problems as well as unique solutions on which we want to shed light on in this session. Each setting introduces its own methodological challenges and potential sources of bias.
We aim to highlight key issues that arise when working with time-to-event data, including common pitfalls, structural complexities, and opportunities for methodological innovation. We will present different perspectives on model choice, interpretation, and discuss how careful consideration of the underlying data-generating mechanisms can inform both statistical analysis and the design of clinically relevant studies.
Speakers
avatar for Marilena Müller

Marilena Müller

German Cancer Research Center, Heidelberg, Germany
Marilena Müller is a postdoctoral scientist in the Biostatistics department at the German Cancer Research Center in Heidelberg, Germany. She received her B.Sc., M.Sc. and Dr. rer. nat. at the Mathematical Institute at Heidelberg University. The field of studies concerning her dissertation... Read More →
avatar for Judith Vilsmeier

Judith Vilsmeier

Institute of Statistics, Ulm University
Judith Vilsmeier is a doctoral candidate in Biostatistics at Ulm University and a research assistant at the Institute of Statistics. She received both her B.Sc. and M.Sc. in Mathematical Biometry from Ulm University. Her current research interests involve nonstandard event histories... Read More →
avatar for Sandra Schmeller

Sandra Schmeller

Institute of Statistics, Ulm University
Sandra Schmeller is a postdoctoral researcher in Biostatistics at the Institute of Statistics, Ulm University. She received both her B.Sc. and M.Sc. in Mathematical Biometry from Ulm University and completed her PhD in Biostatistics at Ulm University. Prior to her doctoral studies... Read More →
avatar for Ema Požek

Ema Požek

Institute for Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Slovenia
Ema Požek is a PhD student in statistics at the Institute for Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana. Her methodological interests lie in survival analysis and simulation studies, and her research focuses on developing statistical methods... Read More →
Chairs/Hosts
avatar for Marilena Müller

Marilena Müller

German Cancer Research Center, Heidelberg, Germany
Marilena Müller is a postdoctoral scientist in the Biostatistics department at the German Cancer Research Center in Heidelberg, Germany. She received her B.Sc., M.Sc. and Dr. rer. nat. at the Mathematical Institute at Heidelberg University. The field of studies concerning her dissertation... Read More →
Organizers
avatar for Marilena Müller

Marilena Müller

German Cancer Research Center, Heidelberg, Germany
Marilena Müller is a postdoctoral scientist in the Biostatistics department at the German Cancer Research Center in Heidelberg, Germany. She received her B.Sc., M.Sc. and Dr. rer. nat. at the Mathematical Institute at Heidelberg University. The field of studies concerning her dissertation... Read More →
S205 Abstracts pdf
Tuesday October 6, 2026 08:00 - 09:00 UTC
Zoom Room #2
  Invited Session, Technical Statistical Session

08:00 UTC

S400 - Building Future Leaders in Statistics and Data Science Japan's Collaborative Model from School Education to Society
Tuesday October 6, 2026 08:00 - 09:00 UTC
Zoom Room #4
As artificial intelligence and data-driven decision-making become integral to society, developing future
leaders in statistics and data science has become an international priority. Beyond teaching statistical
methods or programming skills, education must cultivate statistical thinking, inquiry, evidence-based
reasoning, and the ability to connect data with real-world decision-making throughout learners'
educational journeys.

Japan has been developing an inquiry-based learning framework that progressively connects statistical
and data science education from primary school to university. This framework encourages learners to
investigate authentic questions using data while strengthening statistical literacy through curriculum
reform, inquiry-based learning, student competitions, and university education and research. It is
supported through collaboration among educational institutions, professional societies, public
organizations, and non-profit organizations.

This session introduces four complementary perspectives on this framework. First, it presents how
long-term educational support by a non-profit organization has contributed to nurturing future
statistical talent. Second, it explores how national and international statistical competitions, including the
Statistical Graph Contest and the ISLP International Poster Competition, inspire students and foster
statistical thinking. Third, it shares innovative practices in inquiry-based data science education in
Japanese secondary schools. Finally, it discusses how statistics and data science education can prepare
hybrid talent capable of integrating disciplinary expertise with AI and emerging technologies across
secondary education, higher education, and lifelong learning.

Through these perspectives, the session aims to share Japan's inquiry-based educational framework
with the international community and to promote dialogue on how statistics and data science education
can cultivate future leaders capable of addressing increasingly complex societal challenges.
Speakers
KT

Kiwa Tomaru

Mathematics Teacher, Nagoya University Junior and Senior High School
Kiwa Tomaru is a mathematics teacher at Nagoya University Junior and Senior High School,  where she has designed and taught the compulsory Data Science course since its introduction in 2022, emphasizing reading data correctly over formula-based instruction — work recognized with... Read More →
YK

Yoko Konishi

Professor, University of Tsukuba
Yoko Konishi is a professor at the University of Tsukuba, specializing in applied econometrics and statistics. She received her Ph.D. in Economics from Nagoya University. Her research uses official statistics, private-sector big data, and large-scale surveys to study consumer behavior... Read More →
Tuesday October 6, 2026 08:00 - 09:00 UTC
Zoom Room #4
  Invited Session

09:00 UTC

S206 - Beyond Simulation: Benchmarking for Method Comparison in Statistics and Machine Learning
Tuesday October 6, 2026 09:00 - 09:30 UTC
Zoom Room #2
When initiating a statistical or machine learning analysis, one of the first and most consequential questions is: which method should be used for a given dataset? Traditional approaches for comparing methods include theoretical derivations and data simulation studies, which provide insight into method performance under controlled conditions. However, these approaches may not fully reflect the complexity of real-world data. Benchmarking—systematic comparison of methods across many real datasets—offers a complementary approach that can improve generalizability and provide practical guidance. Despite its common use in computer science, benchmarking remains underutilized in statistical methodology as new methods continue to emerge.

In this talk, I will discuss benchmarking in the context of statistical and machine learning research and contrast it with theory and simulation. I will outline key principles for conducting rigorous benchmarking studies and illustrate them using two case studies: benchmarking random forest variable selection methods for categorical and continuous outcomes and comparing methods for time-to-event data using the mlr3 framework. Together, these examples demonstrate how benchmarking can enhance scientific rigor, provide practical guidance for method selection, and support more transparent and reproducible methodological research.
Speakers
avatar for Jaime Speiser

Jaime Speiser

Associate Professor of Biostatistics and Data Science, Wake Forest University School of Medicine
Dr. Speiser is a biostatistician focused on prediction modeling with applications in medicine. Her work involves developing novel machine learning methodology for prediction modeling, providing guidance on best practices for developing prediction models, and collaborating with medical... Read More →
Organizers
avatar for Jaime Speiser

Jaime Speiser

Associate Professor of Biostatistics and Data Science, Wake Forest University School of Medicine
Dr. Speiser is a biostatistician focused on prediction modeling with applications in medicine. Her work involves developing novel machine learning methodology for prediction modeling, providing guidance on best practices for developing prediction models, and collaborating with medical... Read More →
Tuesday October 6, 2026 09:00 - 09:30 UTC
Zoom Room #2
  Plenary Talk, Technical Statistical Session

09:00 UTC

S401 - Health and Biostatistics 1
Tuesday October 6, 2026 09:00 - 10:00 UTC
Zoom Room #4
A Background Correction and Normalisation Framework for multiplex Immunofluorescence Spatial Proteomics | Menopause-related heterogeneity in lipoprotein(a) effect on atherosclerotic cardiovascular disease: a sex- and age-stratified Mendelian Randomization study in the UK Biobank | Income, Inequality, and Mortality: A Harmonized Comparative Framework Across Ecuador, Colombia, and Brazil|Absorbing Markov Chain Parameter Estimation Under Data Scarcity: A Comparative Study of Analytical and Monte Carlo Methods in Neonatal Care|Predicting the Right Treatment for the Right Patient: An AI-Powered Decision Support Framework Based on Predicted Individual Treatment Effects
Speakers
avatar for Malvika Kharbanda

Malvika Kharbanda

SAiGENCI
Malvika is a PhD candidate in computational oncology at Adelaide University (Australia), where she develops statistical and computational methods to analyse complex biomedical data. Her research focuses on using large scale clinical and spatial imaging datasets to better understand... Read More →
avatar for Gabriela Sandoval

Gabriela Sandoval

CISeAL - PUCE
PhD in Statistics, currently working as a postdoctoral researcher at the Center for Research on Health in Latin America (CISeAL), where she contributes to projects aimed at reducing health inequalities and mitigating the impact of economic crises in the region. Her expertise centers... Read More →
avatar for Daianna Gonzalez Padilla

Daianna Gonzalez Padilla

University of Cambridge
Researcher in Genetic Epidemiology at the MRC Biostatistics Unit, University of Cambridge, specializing in Mendelian randomization, a causal inference framework that uses genetic variants to assess the effects of putative risk factors on disease from observational data. My research... Read More →
avatar for Leandra Braeuninger

Leandra Braeuninger

University College London
Leandra Bräuninger (they/them; she/her) is a doctoral student at University College London, supervised by Dr Brieuc Lehmann and Prof. Ioanna Manolopoulou, using statistical and machine learning methods to define, quantify and mitigate genomic inequity. Drawing on algorithmic fairness... Read More →
Organizers
avatar for IDWSDS Program Committee
S401 Abstracts pdf
Tuesday October 6, 2026 09:00 - 10:00 UTC
Zoom Room #4
  Contributed Session, Technical Statistical Session

09:00 UTC

S110 - Advances in Multi-Source Statistics
Tuesday October 6, 2026 09:00 - 10:00 UTC
Zoom Room #1
This session is organized by the European Survey Research Association – Special Interest Group on multi-source statistics.
As survey research increasingly operates in a complex data ecosystem, integrating probability-based surveys with administrative records, digital traces, non-probability samples, and other non-traditional data sources has become essential for improving inference and studying phenomena that cannot be adequately measured by a single data source. In particular, new data sources can provide better data about hard-to-reach populations and minorities, helping to make these groups more visible in research and evidence-based decision-making. This session will discuss cutting-edge methodologies and innovative case studies that integrate diverse data sources in survey research, with a particular focus on their potential to improve the measurement of underrepresented populations.
Speakers
avatar for Monica Pratesi

Monica Pratesi

University of Pisa
Monica Pratesi is Full Professor of Statistics at the University of Pisa and a member of the Scientific Council of the Italian National Research Council (CNR). She served as President of the International Association of Survey Statisticians (IASS) from 2021 to 2023 and as Director... Read More →
avatar for Char Hilgers

Char Hilgers

German Institute for Economic Research (DIW Berlin), Socio-Economic Panel
Char Hilgers is a PhD student in Sociology at the Humboldt University's Berlin Graduate School of Social Science, funded by the Socio-Economic Panel at DIW Berlin. Their research is on statistical techniques for nonresponse in survey settings: when missingness means something. From... Read More →
avatar for Jisu Kim

Jisu Kim

Utrecht University
Dr. Jisu Kim is currently an assistant professor at Utrecht University, the Netherlands in the department of Interdisciplinary Social Science. She holds a PhD in Data Science from Scuola Normale Superiore in Italy. Prior to her current position, she was a research scientist at Max... Read More →
avatar for Manuela Schmidt

Manuela Schmidt

RPTU Kaiserslautern-Landau
Manuela Schmidt is a researcher at RPTU Kaiserslautern-Landau, where she works on the German Longitudinal Environmental Study (GLEN). Her research focuses on survey methodology, geodata integration, and data quality in quantitative social research.
Chairs/Hosts
CS
Organizers
CS
S110 Abstracts pdf
Tuesday October 6, 2026 09:00 - 10:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session

09:00 UTC

S305 - The Role of Data Modelling for Better Outcomes in Women’s Health
Tuesday October 6, 2026 09:00 - 10:00 UTC
Zoom Room #3
Dr Rachael Duncan will introduce the session that brings together leading speakers to explore how advanced data modelling can improve outcomes in women’s health, particularly through equity-focused and locally led approaches. The session also highlights the importance of women as data scientists and modellers to ensure better healthcare outcomes. Dr Halima Twabi highlights capacity building in Sub-Saharan Africa, emphasising strengthening local expertise, supporting women data scientists, and applying advanced statistical and causal inference methods to complex health challenges. Dr Annabel Sowemimo contributes a critical perspective on structural inequalities, addressing racism and the need to decolonise healthcare systems. Dr Lucy Teece focuses on with insights from industry on the role of biometrics in cancer drug development. Together, the speakers demonstrate how inclusive, collaborative modelling approaches can generate more relevant, impactful, and equitable evidence for women’s health research and policy.
Speakers
avatar for Rachael Duncan

Rachael Duncan

BioSS
Rachael Duncan is a Statistician in Animal Health and Welfare at BioSS. Since joining BioSS in 2023, she has applied statistical methods to a wide range of challenges in animal health and welfare. Prior to this, she completed her PhD at Lancaster University, where she explored how... Read More →
avatar for Halima Twabi

Halima Twabi

University of Malawi
Halima Twabi is an Associate Professor of Statistics at the Department of Mathematical Sciences, University of Malawi. Her research interests are in causal inference for observational data, multivariate statistics, longitudinal and survival analysis, and statistical modelling and... Read More →
avatar for Annabel Sowemimo

Annabel Sowemimo

King’s College London
Dr Annabel Sowemimo is a doctor, academic, activist and writer, and an NHS Consultant in Sexual & Reproductive Health in South London. Her first book, Divided: Racism, Medicine and Why We Need to Decolonise Healthcare (2023), won the Bread and Roses Award for Radical Publishing and... Read More →
avatar for Lucy Teece

Lucy Teece

AstraZeneca
Dr Teece is an Associate Director of Statistical Science at AstraZeneca. Previously, she was a Lecturer in Medical Statistics in the Department of Health Sciences at the University of Leicester. She was awarded her PhD on “Investigating the presence and impact of competing events... Read More →
Chairs/Hosts
avatar for Rachael Duncan

Rachael Duncan

BioSS
Rachael Duncan is a Statistician in Animal Health and Welfare at BioSS. Since joining BioSS in 2023, she has applied statistical methods to a wide range of challenges in animal health and welfare. Prior to this, she completed her PhD at Lancaster University, where she explored how... Read More →
Organizers
EJ
S304 Abstracts pdf
Tuesday October 6, 2026 09:00 - 10:00 UTC
Zoom Room #3
  Invited Session, Technical Statistical Session

09:30 UTC

S207 - On similarity-based models for Bayesian disease mapping
Tuesday October 6, 2026 09:30 - 10:00 UTC
Zoom Room #2
In the 1970s, the conditionally formulated Gaussian Markov random field (GMRF), known as the conditional autoregressive (CAR) model, was introduced in line with Tobler’s first law of geography: "everything is related to everything else, but near things are more related than distant things." The CAR model uses W, the well-known adjacency matrix that encodes the neighbourhood structure of a spatial lattice (e.g., two areas are neighbours if they share a common border).
Since its introduction, the CAR model has undergone many adaptations, with numerous adaptive models proposed in the literature. However, almost all of these models (to the best of our knowledge, all except ours) still adhere to Tobler’s law. Yet, much of the data collected today – often aggregated at the areal level for confidentiality or other reasons – does not necessarily follow this law.
In this talk, we will show that any extra information representing causes of or correlated with the phenomenon of interest can be used to define a similarity structure, rather than relying solely on geographical neighbourhoods. Using simulated data, we illustrate that similarity-based structures can be more effective than traditional neighbourhood-based structures for smoothing both local and global risks. We will show that the correct identification of high- and low-risk areas, crucial for public health planning and resource allocation, is better achieved when the similarity-based structure is used.
Speakers
avatar for Helena Baptista

Helena Baptista

Management Information Centre (MagIC), NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal
Helena Baptista is a highly experienced statistician, researcher, and educator, specializing in applied statistics, forecasting, and time series analysis. She has over 25 years of experience in the pharmaceutical industry, finance, and academia, with a strong background in statistical... Read More →
Organizers
avatar for Helena Baptista

Helena Baptista

Management Information Centre (MagIC), NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal
Helena Baptista is a highly experienced statistician, researcher, and educator, specializing in applied statistics, forecasting, and time series analysis. She has over 25 years of experience in the pharmaceutical industry, finance, and academia, with a strong background in statistical... Read More →
Tuesday October 6, 2026 09:30 - 10:00 UTC
Zoom Room #2
  Plenary Talk, Technical Statistical Session

10:00 UTC

S208 - Optimal Model selection for incidence of Birth Asphyxia: NICU Centers in Greater Accra Region.
Tuesday October 6, 2026 10:00 - 10:30 UTC
Zoom Room #2
Abstract
Birth asphyxia remains a major contributor to neonatal morbidity and mortality in low- and middle-income countries, particularly in sub-Saharan Africa. This study investigated the determinants of birth asphyxia among newborns using a Quasi-Poisson regression model to account for overdispersion in the count data. Secondary data comprising neonatal and maternal records were analysed using descriptive statistics, correlation analysis, and inferential modelling. The Quasi-Poisson model selected after diagnostic assessment confirmed overdispersion in the response variable, making it more appropriate than the standard Poisson model. The Quasi-Poisson regression model for predicting birth asphyxia is given as : Birth Asphyxia = 1.742 +0.385(Birth Weight) + 0.012(Mothers Age)− 0.088(Gestational Age) + 0.0401(Sex)+ 0.301(Mode of Delivery) + 0.158(Parity)+ 0.067(SURVIVE)
The results showed that birth weight, gestational age, mode of delivery, maternal age, parity, and fetal presentation were significant predictors of birth asphyxia. Specifically, lower birth weight and shorter gestational age were associated with a higher incidence of birth asphyxia, while caesarean delivery and abnormal fetal presentation increased the likelihood of adverse birth outcomes.
Keywords: Birth asphyxia, Quasi-Poisson regression, Gestational age, Neonatal outcomes, Maternal health, Ghana.
Authors:
1. Selina Dadzie (Mphil)
2. Irene Kafui Vorsah Amponsah (PhD)
Speakers
avatar for Selina Dadzie

Selina Dadzie

Student
Bio
Miss Selina Dadzie is a graduate student in Statistics at the University of Cape Coast, Ghana, where she is pursuing a Master of Philosophy (MPhil) in Statistics. Her research focuses on “Optimal Model Selection for Incidence of Birth Asphyxia: NICU Centers in Accra”, with particular... Read More →
Organizers
avatar for Selina Dadzie

Selina Dadzie

Student
Bio
Miss Selina Dadzie is a graduate student in Statistics at the University of Cape Coast, Ghana, where she is pursuing a Master of Philosophy (MPhil) in Statistics. Her research focuses on “Optimal Model Selection for Incidence of Birth Asphyxia: NICU Centers in Accra”, with particular... Read More →
Tuesday October 6, 2026 10:00 - 10:30 UTC
Zoom Room #2
  Plenary Talk, Technical Statistical Session

10:00 UTC

S111- Strengthening Statistical Practice Through Collaboration: Perspectives from Women Leaders in STRATOS
Tuesday October 6, 2026 10:00 - 11:00 UTC
Zoom Room #1
The STRATOS (STRengthening Analytical Thinking for Observational Studies) Initiative is an international collaboration of statistical and methodological experts dedicated to improving the design, conduct, analysis, and reporting of biomedical and observational research. Through accessible guidance documents, recommendations, and educational resources, STRATOS seeks to bridge methodological advances and statistical practice.
Presentations illustrate diverse approaches to improving research quality, including the development of international recommendations for analyzing patient-reported outcomes in oncology trials, innovative strategies for teaching Initial Data Analysis (IDA), consensus-based development of guidance for statistical analysis plans in observational studies, and principled approaches to defining estimands and interpreting quality-of-life outcomes. These examples demonstrate how statistical thinking can improve the transparency, reproducibility, relevance, and interpretability of scientific research.
Together, these talks demonstrate the impact of international collaboration, interdisciplinary research, translating complex statistical concepts into practical tools for researchers. The session also highlights the important leadership role of women statisticians in shaping methodological standards, educational initiatives, and research practices.
Speakers
avatar for Marianne Huebner

Marianne Huebner

Michigan State University
Marianne Huebner is Professor of Statistics and Director of the Center for Statistical Training and Consulting (CSTAT) at Michigan State University, USA. Her research focuses on statistical methodology for improving transparency, reproducibility, and quality in health and observational... Read More →
avatar for Saskia Le Cessie

Saskia Le Cessie

Leiden University Medical Centre
Prof. Saskia le Cessie is a medical statistician working at the departments of Clinical Epidemiology and Biomedical Data Sciences of Leiden University Medical Center. Her research in medical statistics and epidemiological methods involves (1) collaboration in research projects of... Read More →
avatar for Els Goetghebeur

Els Goetghebeur

Ghent University
Els Goetghebeur is Professor of Statistics at Ghent University, Belgium, and a leading researcher in causal inference and biostatistical methodology. She co-chairs the Causal Inference Topic Group of the STRATOS Initiative and has contributed extensively to methodological research... Read More →
avatar for Lara Lusa

Lara Lusa

University of Primosrka
Lara Lusa is a professor of Statistics at the Faculty of Mathematics, Natural Sciences and Information Technologies of the University of Primorska, Slovenia and Institute for Biostatistics and Medical Informatics of the University of Ljubljana, Slovenia. Her current research interests... Read More →
Chairs/Hosts
avatar for Marianne Huebner

Marianne Huebner

Michigan State University
Marianne Huebner is Professor of Statistics and Director of the Center for Statistical Training and Consulting (CSTAT) at Michigan State University, USA. Her research focuses on statistical methodology for improving transparency, reproducibility, and quality in health and observational... Read More →
Organizers
avatar for Marianne Huebner

Marianne Huebner

Michigan State University
Marianne Huebner is Professor of Statistics and Director of the Center for Statistical Training and Consulting (CSTAT) at Michigan State University, USA. Her research focuses on statistical methodology for improving transparency, reproducibility, and quality in health and observational... Read More →
S111 Abstracts pdf
Tuesday October 6, 2026 10:00 - 11:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session

10:00 UTC

S306 - Modern Statistical Methods and Applications in Health Research
Tuesday October 6, 2026 10:00 - 11:00 UTC
Zoom Room #3
This session highlights the breadth of biostatistics through four distinct but complementary talks by statistical researchers. It explores how modern statistical methodologies are advancing health research by addressing challenges in evidence generation, clinical decision-making, and causal inference. The presentations cover Bayesian learning frameworks for individualised treatment recommendations, large language model-assisted tools that improve the efficiency and reproducibility of systematic reviews and meta-analyses, innovative clinical trial designs and the practical challenges that influence their efficiency, and Mendelian randomisation approaches for investigating causal relationships and therapeutic targets in cardiovascular disease. Together, these talks aim to strengthen the reliability, transparency and translation of evidence, thereby supporting more informed clinical and public health decision-making in an increasingly connected global health research landscape.
Speakers
avatar for Pamela Massiel Chiroque Solano

Pamela Massiel Chiroque Solano

University of Regensburg
Pamela Solano is a statistician passionate about transforming complex data into trustworthy clinical decisions. As a researcher at the Faculty of Informatics and Data Science, University of Regensburg, Germany, she develops interpretable AI and Bayesian learning methods that empower... Read More →
avatar for Danyang Dai

Danyang Dai

The University of Sydney
Danyang Dai (Daidai) is a final year PhD student (thesis submitted) at the Queensland Digital Health Centre. She is an applied statistician and infectious disease epidemiology researcher with expertise in large scale health data analysis, disease surveillance, and advanced statistical... Read More →
avatar for Aritra Mukherjee

Aritra Mukherjee

Newcastle University
Aritra Mukherjee is a Research Associate in the Biostatistics Research Group at Newcastle University. Her research focuses on developing innovative statistical methods for clinical trials, with expertise in adaptive trial designs such as multi-arm multi-stage (MAMS) trials, sample... Read More →
avatar for Daianna Gonzalez Padilla

Daianna Gonzalez Padilla

University of Cambridge
Researcher in Genetic Epidemiology at the MRC Biostatistics Unit, University of Cambridge, specializing in Mendelian randomization, a causal inference framework that uses genetic variants to assess the effects of putative risk factors on disease from observational data. My research... Read More →
Chairs/Hosts
RD
Organizers
RD
S305 Abstracts pdf
Tuesday October 6, 2026 10:00 - 11:00 UTC
Zoom Room #3
  Invited Session, Technical Statistical Session

10:00 UTC

S402 - Selected Topics in Statistical Methods and Applications
Tuesday October 6, 2026 10:00 - 12:00 UTC
Zoom Room #4
Beyond the Average: Random Slope Models for Revealing Territorial and Gender Inequalities | Randomization Inference on Policy Assignments | Becker’s models for mixture experiments: An A-optimal approach | Inference for GMANOVA model for Volatile Data
Speakers
avatar for Naomi Diz Rosales

Naomi Diz Rosales

PhD Student in Statistics, Universidade da Coruña
I hold a PhD in Statistics from the Universidade da Coruña (UDC), where my research focused on the development of mixed models with random slopes for Small Area Estimation. My work combines statistical methodology with applications to socioeconomic and public health challenges, including... Read More →
avatar for EunYi Chung

EunYi Chung

University of Illinois at Urbana-Champaign
She is an Associate Professor of Economics at the University of Illinois Urbana-Champaign, specializing in econometric theory. Her research focuses on developing improved methods of statistical inference for evaluating the causal effects of policy interventions using both experimental... Read More →
avatar for Bushra Husain

Bushra Husain

ALIGARH MUSLIM UNIVERSITY, ALIGARH, U.P., INDIA
Bushra Husain, Professor is the Chairperson of Department of Statistics and Operations Research, Aligarh Muslim University, Aligarh, U.P., India and has been working as Faculty of Statistics at Women's College since 2003. She has obtained B.Sc., M.Sc., M.Phil., and Ph.D. in Statistics... Read More →
avatar for Sudeepta Pal

Sudeepta Pal

PhD Scholar at IIT Hyderabad
I am currently pursuing my PhD in Statistics at IIT Hyderabad, where I also work as a Teaching Assistant. My research mainly focuses on statistical theory, multivariate analysis, and computational statistics. I cleared two of India's toughest competitive exams with high ranks, securing... Read More →
Organizers
avatar for IDWSDS Program Committee
S402 Abstracts pdf
Tuesday October 6, 2026 10:00 - 12:00 UTC
Zoom Room #4
  Contributed Session, Technical Statistical Session

10:30 UTC

S209 - Temporal and Regional Variations of Effects of Daily Temperature on Annual Precipitation: A Functional Mixed Effect Model Approach
Tuesday October 6, 2026 10:30 - 11:00 UTC
Zoom Room #2
"Given the increasing threat of global warming, it is important to understand not only
global weather patterns but also regional variations throughout countries. Functional
Linear Mixed-effects Model (FLMM), an emerging statistical tool, provides a comprehensive
framework for analyzing functional data (e.g., data viewed as a function
or curve) with repeated observations, allowing researchers to identify patterns and relationships
in the data. This study applies the functional linear mixed-effects model
(FLMM) to recognize the effects of temporal and regional variations of short-time
weather projection (monthly precipitation on daily temperature). We deployed FLMM
using the daily temperature and monthly precipitation of nine weather stations(Dhaka,
Chattogram (Patenga), Chattogram (Ambagan), Rajshahi, Khulna, Barisal, Sylhet,
Rangpur and Mymensingh) of Bangladesh where each station shares the common
population effects with their individual scalar covariate effects along with same slope
functions. To estimate variance parameters and fixed-effects and random effects, the
REML-based EM algorithm proposed by has been applied. Empirical
results show significant differences in the effects of monthly precipitation on daily
temperature among regions of Bangladesh. We anticipated that FLMM is an emerging
model that can reveal the effects of monthly precipitation and the pace of daily
temperature fluctuations over time."
Speakers
avatar for Munniara Yesmin Munni

Munniara Yesmin Munni

PME Officer
Munniara Yesmin Munni is a statistician and Monitoring, Evaluation, and Learning (MEL) professional with a Master’s degree in Statistics from Jahangirnagar University, Bangladesh. She currently works with the Christian Commission for Development in Bangladesh (CCDB). Her research... Read More →
Organizers
avatar for Munniara Yesmin Munni

Munniara Yesmin Munni

PME Officer
Munniara Yesmin Munni is a statistician and Monitoring, Evaluation, and Learning (MEL) professional with a Master’s degree in Statistics from Jahangirnagar University, Bangladesh. She currently works with the Christian Commission for Development in Bangladesh (CCDB). Her research... Read More →
Tuesday October 6, 2026 10:30 - 11:00 UTC
Zoom Room #2
  Plenary Talk, Technical Statistical Session

11:00 UTC

K3
Tuesday October 6, 2026 11:00 - 12:00 UTC
Zoom Room #1

Speakers
avatar for Dootika Vats

Dootika Vats

Department of Statistics and Data Science, IIT Kanpur

Organizers
avatar for IDWSDS Program Committee
Tuesday October 6, 2026 11:00 - 12:00 UTC
Zoom Room #1
  Keynote Session

12:00 UTC

S112 - Predicting the Right Treatment for the Right Patient: An AI-Powered Decision Support Framework Based on Predicted Individual Treatment Effects
Tuesday October 6, 2026 12:00 - 12:30 UTC
Zoom Room #1
Medical decisions—ranging from diagnosis to treatment selection—are inherently uncertain. Clinicians often rely on heuristic, experience-driven processes to integrate heterogeneous data. In this context, Predicted Individual Treatment Effects (PITE) offer a principled statistical framework to quantify how much a specific patient benefits from one treatment over another.

Advances in computational systems now enable machines to identify complex patterns within large datasets, facilitating a shift toward data-driven, individualized healthcare. This work explores using PITE to support clinical decision-making across diverse diseases and contexts. We address critical questions: which AI methods suit specific clinical datasets, how PITE should be validated, and how outcome complexity affects tool reliability.

We demonstrate PITE-based models in various disease settings, each posing unique methodological challenges. Our results show that even under real-world conditions—such as missing data—predictive models maintain interpretability and generate estimates that support clinicians. Notably, our findings highlight that internal validation is insufficient; external validation is essential for robust predictions.

Ultimately, effective PITE-based support requires more than modeling. It demands an adaptive, continuously learning system integrating data management, modeling strategies, regulatory-grade interpretability, and ongoing validation to translate evidence into precise.
Speakers
avatar for Pamela Solano

Pamela Solano

PhD Researcher, Faculty of Computer Science and Data Science, Regensburg University
I am Pamela Solano, a statistician and researcher at the University of Regensburg, Germany. Since 2014, I have worked as a biostatistician. Following my PhD in 2018, my research focus toward statistical modeling approaches with direct societal relevance. I began working in environmental... Read More →
Organizers
avatar for Pamela Solano

Pamela Solano

PhD Researcher, Faculty of Computer Science and Data Science, Regensburg University
I am Pamela Solano, a statistician and researcher at the University of Regensburg, Germany. Since 2014, I have worked as a biostatistician. Following my PhD in 2018, my research focus toward statistical modeling approaches with direct societal relevance. I began working in environmental... Read More →
Tuesday October 6, 2026 12:00 - 12:30 UTC
Zoom Room #1
  Plenary Talk, Technical Statistical Session

12:00 UTC

S501 - Additional Topics in AI and Data Science 1
Tuesday October 6, 2026 12:00 - 13:00 UTC
Zoom Room #5
Graph-Aware Sparse Regression for Block-Missing Multimodal Neuroimaging Data | Decoding Antartic Microbial Biodiversity through Hybrid Machine Learning and Constrained Ordination | Assessment of Methodological Quality in Non-Commercial Clinical Studies: The Impact of International Reporting Guidelines on Studies Approved
Speakers
avatar for Gabriella Tarantini

Gabriella Tarantini

Biologist, IRCCS Azienda Ospedaliero-Universitaria di Bologna
RESEARCH FELLOWSHIP & THESIS PROJECT, McMaster University, Department of Health Research Methods, Evidence, and Impact (HEI), Hamilton, Canada, from JUN/2026 to OCT/2026.
Developed and implemented statistical models to identify and evaluate predictive factors of methodological q... Read More →
avatar for Mutiyat Usman

Mutiyat Usman

North Carolina A&T State University
Mutiyat Adeola Usman is a Doctoral Researcher in Data Science and Analytics at North Carolina A&T State University, specializing in quantitative methodology, statistical modeling, and machine learning for complex, high-dimensional data. Her research focuses on developing Dynamic Gaussian... Read More →
avatar for Silvia María Herranz Hernández

Silvia María Herranz Hernández

University College London
I graduated with two bachelor's degrees, in Mathematics and Computer Science, from Universidad Autónoma de Madrid, having also studied at the Università degli Studi di Torino. In September 2026 I started an MSc in Artificial Intelligence for Biomedicine and Healthcare at University... Read More →
Organizers
avatar for IDWSDS Program Committee
S501 Abstracts pdf
Tuesday October 6, 2026 12:00 - 13:00 UTC
Zoom Room #5
  Contributed Session, Technical Statistical Session

12:00 UTC

S307 - Creating Alliances through a Data Feminism Community of Practice
Tuesday October 6, 2026 12:00 - 13:00 UTC
Zoom Room #3
How has the Global Data Feminism Community of Practice (DFCoP) supported data practitioners and statisticians in leading inclusive practices in data collection and uptake? This question will guide the discussion in this session, which will feature 4 of the more than 150 members of the DFCoP. This session will introduce the DFCoP as a Global Majority international community, where practitioners, statisticians, researchers, and civil society from all around the world exchange experiences and knowledge on how to put data feminism into practice. The session will begin by contextualizing the community and speakers’ strategic work in the fields of statistics and data feminism; as well as the practical ways in which members are co-creating a new network to continue the dialogue around feminist data practices and strengthen knowledge exchange. The session will then focus on the unique experiences of community members, including statisticians and project implementers. They will share some of their learnings and experiences on how they are applying data feminism into their statistical and data work. During the session, we will highlight the value of communities of practice like the DFCoP, and how members are not only practicing data feminism, but also collectively pushing the boundaries of this sector to advance equitable and accurate data practices. The session will conclude with a call to action, inviting attendees to join the community.
Speakers
avatar for Dr. Lorena Fuentes

Dr. Lorena Fuentes

Co-founder, Ladysmith Collective
Lorena’s research explores how different types of data shape visibility and action in development, with a focus on gender-related violence. Currently, she is co-authouring a book about the ‘gender data revolution’ with her co-founder and feminist-thought-partner-in-crime, Dr... Read More →
avatar for Lucine Kalantaryan

Lucine Kalantaryan

Statistical Committee of the Republic of Armenia (ARMSTAT)
Lucine Kalantaryan is the Head of the Labor Statistics Division and Gender Statistics Focal Point at the Statistical Committee of the Republic of Armenia (ARMSTAT), where she has coordinated the production and development of official statistics since 2002. An economist by training... Read More →
avatar for Anahit Simonyan

Anahit Simonyan

GIZ Armenia
Anahit Simonyan is a feminist researcher and human rights practitioner based in Armenia, with over 10 years of experience in combating gender-based violence and discrimination. Throughout her work she has cooperated with numerous local and international organizations and networks... Read More →
avatar for Eleonora Sironi

Eleonora Sironi

Researcher, Ladysmith Collective
Eleonora’s research focuses on the intersections between gender, technology, and digitization. She brings cross-sectoral experience through work with NGOs like Doctors Without Borders and tech start-ups like VIOLA, an app focused on addressing gender-based violence. As a researcher... Read More →
Chairs/Hosts
KS
Organizers
KS
Tuesday October 6, 2026 12:00 - 13:00 UTC
Zoom Room #3
  Invited Panel, Other

12:00 UTC

S210 - Digitalisation of public institutions in low resource settings.
Tuesday October 6, 2026 12:00 - 13:00 UTC
Zoom Room #2
Digitalizing public institutions in low-resource settings requires balancing technological innovation with systemic equity. Across Africa, infrastructure bottlenecks and high deployment costs often stall digital transformation. This session examines how public sector agencies can successfully transition from fragmented, legacy offline systems to unified digital platforms. We will discuss scalable hybrid architectures that maintain crucial offline functionality for underserved communities while enforcing centralised online data validation, optimizing institutional workflows, and improving financial reconciliation.
Speakers
avatar for Louisa Muparuri

Louisa Muparuri

ZIMSEC
Dr. Louisa Muparuri is an AI researcher, ICT executive, and digital transformation leader with expertise in artificial intelligence, data science, mathematical modelling, computing, examination systems, and public-sector innovation. She holds a PhD in Mathematics, an MSc in Operations... Read More →
avatar for Phyliss Shamiso Mavedzenge

Phyliss Shamiso Mavedzenge

Pulse Pharmaceuticals
Ms Phyliss Mavedzenge. Currently a Resident Innovator and Researcher at Zimbabwe Centre for High Performance Computing and a Business Analytics and Predictive Modelling, short course facilitator at Bindura University . Working on developing systems and models that cater to Zimbabweans... Read More →
avatar for Salome Maheya

Salome Maheya

African Energy Commission of the African Union
Salome Maheya, Senior Policy Officer for Energy Statistics in the African Energy Commission (AFREC) of the African Union. She is responsible for coordinating the production of annual Energy Balances, Energy Modelling and producing energy forecasts for Africa. She is responsible for... Read More →
avatar for Lerdinia Mapepa

Lerdinia Mapepa

Women's University in Africa
Ms. L. V. Mapepa is an Academic in the Faculty of Management, Department of Information Systems at the Women’s University in Africa where she has served the institution for over five years across the various fields of teaching, learning and Innovation, Research and STEM initiatives... Read More →
Chairs/Hosts
avatar for Chipo Zidana

Chipo Zidana

NUST
Dr. Chipo Zidana is a senior statistician, bio-statistical consultant, and data integration specialist with over 15 years of experience bridging advanced analytical modeling with national policy frameworks. She holds a PhD in Statistics from Çukurova University, specializing in mixture... Read More →
Organizers
avatar for Chipo Zidana

Chipo Zidana

NUST
Dr. Chipo Zidana is a senior statistician, bio-statistical consultant, and data integration specialist with over 15 years of experience bridging advanced analytical modeling with national policy frameworks. She holds a PhD in Statistics from Çukurova University, specializing in mixture... Read More →
S210 Abstracts pdf
Tuesday October 6, 2026 12:00 - 13:00 UTC
Zoom Room #2
  Invited Session, Technical Statistical Session
  • Session ID 210

12:00 UTC

S403 - Change point detection and its applications
Tuesday October 6, 2026 12:00 - 13:00 UTC
Zoom Room #4
Change point detection has long been an active and important area of statistical research because structural changes frequently arise in data collected over time or across different conditions. Detecting these changes helps researchers better understand complex processes and make more informed decisions in fields such as public health, finance, genomics, environmental science, manufacturing, and engineering, where the ability to detect shifts in trends, variability, or dependence structures can provide valuable insights into complex systems. As the volume and complexity of modern data continue to grow, the development of robust and efficient change point techniques remains a significant research challenge and opportunity. This invited session brings together researchers from three countries to present new methods and real-world applications of change point analysis. The session will highlight recent advances in the field, encourage international collaboration, and showcase the broad impact of change point research across diverse disciplines.
Speakers
avatar for Rebecca Killick

Rebecca Killick

Clemenson University
Rebecca Killick is Associate Director of Research in Mathematical and Statistical Sciences at Clemson University. Dr. Killick received a PhD in Statistics from Lancaster University, where until 2026, held a Professor and Director of Research positions. In 2019 Dr. Killick was the... Read More →
avatar for Kun Liu

Kun Liu

JPMorgan Chase & Company
Kun Liu is a Vice President and Quant Modeling Lead at JPMorgan Chase with expertise in AI, machine learning, credit risk, fraud detection, and causal inference. He holds a Ph.D. in Statistics from Georgia Tech and has over eight years of experience developing and deploying advanced... Read More →
avatar for Meenu Rani

Meenu Rani

Indian Institute of Technology Ropar
Meenu Rani is a Ph.D. student in the Department of Mathematics at the Indian Institute of Technology Ropar. Her doctoral research focuses on change point detection, including both offline and online settings. Her work has primarily involved applications to financial data.
avatar for Ayten Yiğiter

Ayten Yiğiter

Hacettepe University
Dr. Yiğiter received the B.S., M.S., and Ph.D. degrees in statistics from Hacettepe University, Ankara, Türkiye, in 1995, 2000 and 2006, respectively. In 2007, she was a Visiting Scholar with the University of Missouri-Kansas City, Kansas City, MO, USA. She is currently working... Read More →
Chairs/Hosts
JC
Organizers
JC
S403 Abstracts pdf
Tuesday October 6, 2026 12:00 - 13:00 UTC
Zoom Room #4
  Invited Session, Technical Statistical Session

12:30 UTC

S113 - Navigating Data Sharing in Medical Research
Tuesday October 6, 2026 12:30 - 13:00 UTC
Zoom Room #1
Open science is pivotal in advancing medical research by promoting accessibility and collaboration among researchers globally, and biostatisticians play a critical role in supporting these efforts. Shared datasets and software code, particularly those developed using time-intensive algorithms, are fundamental for fostering replicability and improving research efficiency for other scientists. Open access publications accompanied by publicly available data and code also help reduce disparities in knowledge access by making research materials available to researchers who might otherwise lack access. This presentation will explore data sharing principles and experiences, highlighting two recent studies: one on smoking behaviors in the United States and another on the association between caregiving and psychological well-being among Florida college students. The resulting datasets were shared under specific terms of use via Harvard Dataverse to facilitate further medical research. In addition, a subset of the tobacco use data and sample code were shared through the Resources Portal of the Teaching of Statistics in the Health Sciences Section of the American Statistical Association to support the teaching of survey methods. The presentation is intended for students, researchers, and practitioners interested in data sharing and open science.
Speakers
avatar for Julia Soulakova

Julia Soulakova

University of Central Florida College of Medicine
Julia Soulakova, Ph.D., is a biostatistician and Professor of Medicine in the Department of Population Health Sciences at the University of Central Florida College of Medicine. Her research interests include statistical methodology with applications to behavioral medicine and social... Read More →
Organizers
avatar for Julia Soulakova

Julia Soulakova

University of Central Florida College of Medicine
Julia Soulakova, Ph.D., is a biostatistician and Professor of Medicine in the Department of Population Health Sciences at the University of Central Florida College of Medicine. Her research interests include statistical methodology with applications to behavioral medicine and social... Read More →
Tuesday October 6, 2026 12:30 - 13:00 UTC
Zoom Room #1
  Plenary Talk, Career or Professional Development Session

13:00 UTC

S114 - Fractional Statistical Models via Operator Theory: A Data-Driven Framework for Aviation Analytics
Tuesday October 6, 2026 13:00 - 13:30 UTC
Zoom Room #1
Classical statistical models are built upon an assumption of short-range dependence — an assumption that fails dramatically when confronted with the complexity of real-world aviation datasets. Such datasets routinely exhibit long-range memory, non-stationarity, and heavy-tailed distributions that render conventional approaches inadequate. In this work, we propose a novel fractional statistical framework that draws on advanced operator theory to directly address these challenges, offering both rigorous theoretical guarantees and compelling empirical improvements over established baselines.
We construct a family of Toeplitz-type estimators grounded in the theory of α-fractional Bergman spaces, establish their theoretical properties, validate the framework on a large-scale aviation dataset comprising over 500,000 UAE flight records, and demonstrate prediction error reductions of 23–31% over ARIMA and 14–18% over LSTM-based approaches.
Speakers
avatar for Raja'a Alnaimi

Raja'a Alnaimi

emirates aviation university
Dr. Raja’a Al-Naimi is an Assistant Professor in the Department of Mathematics
and Data Science at Emirates Aviation University (EAU), Dubai, UAE. She holds
expertise in operator theory, fractional calculus, and functional analysis, with active
research programs in α-fracti... Read More →
Organizers
avatar for Raja'a Alnaimi

Raja'a Alnaimi

emirates aviation university
Dr. Raja’a Al-Naimi is an Assistant Professor in the Department of Mathematics
and Data Science at Emirates Aviation University (EAU), Dubai, UAE. She holds
expertise in operator theory, fractional calculus, and functional analysis, with active
research programs in α-fracti... Read More →
Tuesday October 6, 2026 13:00 - 13:30 UTC
Zoom Room #1
  Plenary Talk

13:00 UTC

S502 - Additional Topics in AI and Data Science 2
Tuesday October 6, 2026 13:00 - 14:00 UTC
Zoom Room #5
Developing and Using Data Visualization Requirements Gathering Processes for Clients | Mapping Global Open-Source Collaboration: A Data Science Approach to Measuring International Software Development | Multimodal AI Fusion for Better Alzheimer Detection | Survival-Informed Digital Twins for Glioblastoma: Integrating cBioPortal Data, Statistical Learning, and Nonlinear PDEs
Speakers
avatar for Chipo Zidana

Chipo Zidana

NUST
Dr. Chipo Zidana is a senior statistician, bio-statistical consultant, and data integration specialist with over 15 years of experience bridging advanced analytical modeling with national policy frameworks. She holds a PhD in Statistics from Çukurova University, specializing in mixture... Read More →
avatar for Adriana Perez

Adriana Perez

The University of Texas Health Science Center at Houston
Professor at the Department of Biostatistics and Data Science. Dr. Pérez is engaged in a wide range of research projects: theoretical model evaluation accounting for imputation uncertainty; fitting complex data; analysis of cluster-randomized community trials; clinical trials; analysis... Read More →
avatar for Sarah Glidden

Sarah Glidden

Westat
Sarah Glidden is a research and data visualization analyst. She specializes in dashboard development for a range of external clients using Tableau.
avatar for Rashi Saluja

Rashi Saluja

Westat
Rashi is a Data Scientist at Westat, where she works across a broad portfolio of research projects spanning survey methodology, public health, applied machine learning, and artificial intelligence. Her recent work includes network analysis of open-source software collaboration, as... Read More →
Organizers
avatar for IDWSDS Program Committee
S502 Abstracts pdf
Tuesday October 6, 2026 13:00 - 14:00 UTC
Zoom Room #5
  Contributed Session, Technical Statistical Session, History of Women Statisticians Session, Career or Professional Development Session, Other

13:00 UTC

S211 - Statistical applications across various domains from India
Tuesday October 6, 2026 13:00 - 14:00 UTC
Zoom Room #2
Statistical applications and data analytics drive critical advancements across major global sectors. By leveraging historical data and predictive modeling, these methodologies improve efficiency, reduce risks, and optimize resource allocation worldwide. This session will focus on how statistical tools and methodologies enable data-driven decision making aacross different domains such as banking, business analytics, agriculture and healthcare in India. We have speakers from academia, government and different industries providing us with an inside journey of their work.
Speakers
avatar for Sumaiya Sande

Sumaiya Sande

Wells Fargo
Dr. Sumaiya Sande is a Data Scientist and Credit Risk modelling professional with a PhD in
Statistics and Applied Probability from the National University of Singapore. She specializes in
machine learning, deep learning, statistical modeling, and credit risk analytics, with exp... Read More →
avatar for Swastika Mohapatro

Swastika Mohapatro

Google
Swastika Mohapatra is a Research Data Scientist on the Analytics, Insights, and Measurement team within Google Ads. Over the past two years, her work has focused on building models to accurately measure digital ad campaign performance, sitting at the intersection of predictive modeling... Read More →
avatar for Yogita Gharde

Yogita Gharde

ICAR–Directorate of Weed Research (ICAR-DWR), Jabalpur, India.
Dr. Yogita Gharde is currently serving as Senior Scientist (Agricultural Statistics) at the
ICAR–Directorate of Weed Research (ICAR-DWR), Jabalpur, Madhya Pradesh, India. She
earned her M.Sc. and Ph.D. in Agricultural Statistics from the ICAR–Indian Agricultural
Statistics... Read More →
avatar for Sharvari Shukla

Sharvari Shukla

Symbiosis Statistical Institute, Symbiosis International University
Sharvari Shukla is a Professor and Director at Symbiosis Statistical
Institute, India. Sharvari is a thought leader and well regarded
influencer working at the intersection of Medical Statistics and Data
Science. In a prior assignment she was leading clinical research and
cli... Read More →
Chairs/Hosts
SJ
Organizers
SJ
S211 Abstracts pdf
Tuesday October 6, 2026 13:00 - 14:00 UTC
Zoom Room #2
  Invited Session, Technical Statistical Session

13:00 UTC

S308 - Pedagogy with Purpose: Tailoring your Statistics Classroom for Specific Audiences
Tuesday October 6, 2026 13:00 - 14:00 UTC
Zoom Room #3
Given the wide variety of opportunities available for learners to gain expertise in statistics and data science, students enter the classroom with different needs and goals. Instructors balance standardized learning outcomes with individualized support, and a happy medium is not always easy to achieve. In this session, sponsored by the Caucus for Women in Statistics and Data Science, we feature a diverse group of educators whose trainees range from high school students just learning first principles to clinicians looking to build biostatistical expertise. The speakers will present their experiences customizing their courses to meet students where they are. First, Dr. Nicole Dalzell will share tips for structuring a first-year statistics course that builds a strong foundation for success in college. Next, Ms. Ashley Mullan will discuss lessons learned from designing a weeklong introductory summer course for high school students. Then, Dr. Taylor Krajewski will describe approaches for adapting communication and content while maintaining rigor, engagement, and accessibility. Finally, Dr. Jamie Joseph will outline considerations for adapting a biostatistics seminar series to provide support for both medical licensure candidates and emerging investigators. The four speakers span a wide array of educational settings, making the session interesting to a general audience. However, all four showcase how thoughtful education can support all members of the global statistics community.
Speakers
avatar for Ashley Mullan

Ashley Mullan

Vanderbilt University
Ashley Mullan is a PhD student and research assistant in the Department of Biostatistics at Vanderbilt University. She earned her MS in Statistics from Wake Forest University. Her research interests include both methods development for measurement error and missing data and their... Read More →
avatar for Jamie Joseph

Jamie Joseph

Henry Ford Health
Dr. Jamie Joseph is a faculty biostatistician in the Department of Public Health Sciences at Henry Ford Health with a joint appointment as an Assistant Professor of Research at Michigan State University. She earned her PhD in Biostatistics from Vanderbilt University in 2024. Currently... Read More →
avatar for Nicole Dalzell

Nicole Dalzell

Wake Forest University
Dr. Nicole Dalzell is an Associate Teaching Professor in the Department of Statistical Sciences at Wake Forest University. She loves developing new courses and constantly thinking about how to make her teaching materials and style work better for students. She also enjoys mentoring... Read More →
avatar for Taylor Krajewski

Taylor Krajewski

Duke University
Dr. Taylor Krajewski is an Assistant Professor in the Department of Biostatistics & Bioinformatics and the Department of Population Health Sciences at the Duke University School of Medicine and a member of the Duke Clinical Research Institute. Her research focuses on causal inference... Read More →
Chairs/Hosts
JR
Organizers
avatar for Ashley Mullan

Ashley Mullan

Vanderbilt University
Ashley Mullan is a PhD student and research assistant in the Department of Biostatistics at Vanderbilt University. She earned her MS in Statistics from Wake Forest University. Her research interests include both methods development for measurement error and missing data and their... Read More →
S307 Abstracts pdf
Tuesday October 6, 2026 13:00 - 14:00 UTC
Zoom Room #3
  Invited Session, Career or Professional Development Session

13:00 UTC

S404 - Coordinating Evidence for Better Research Design: Priors, Assumptions, and Scientific Judgment
Tuesday October 6, 2026 13:00 - 14:00 UTC
Zoom Room #4
This session examines how scientific judgment can be made explicit in research design and decision making with mixed methods evidence, causal knowledge, expert elicitation, and Bayesian priors. Noor Qaragholi opens by showing how qualitative and quantitative evidence can inform strong, untestable causal assumptions in study design, illustrated through CACE estimation in a randomized behavioral intervention with substantial noncompliance. Utkarshani Jaimini then turns to Causal Neuro-Symbolic AI, a framework for building systems that are explainable, interpretable, and causally aware rather than limited to correlational pattern matching. Her work spans knowledge representation, causal inference, and applied machine learning. Anna Heath discusses expert elicitation for Bayesian clinical trial design, focusing on remote, real time exercises that help experts translate experience into probabilistic statements for prior distributions. Lu (Maggie) Qian closes by examining how Bayesian trial design can make scientific judgment explicit without allowing prior choice to become opaque, drawing on FDA guidance and reverse-Bayes methods as an audit tool. Together, the talks show how making assumptions and priors explicit can strengthen research design and support credible, decision relevant conclusions.
Speakers
avatar for Noor Qaragholi

Noor Qaragholi

The Policy & Research Group
Noor Qaragholi is a public health researcher with 15 years of experience conducting qualitative, quantitative, and mixed methods research to improve health programs, services, and systems. As a Senior Research Analyst at The Policy & Research Group, Dr. Qaragholi leads large-scale... Read More →
avatar for Utkarshani Jaimini

Utkarshani Jaimini

University of Michigan Dearborn
Utkarshani Jaimini is an Assistant Professor in the Department of Computer and Information Science at the University of Michigan-Dearborn, where she joined as a faculty in Fall 2025. She holds her doctoral degree from the University of South Carolina. Her research lies at the intersection... Read More →
avatar for Lu Qian

Lu Qian

Zetyra, LLC
Lu (Maggie) Qian is an independent biostatistician and founder of Evidence in the Wild and Zetyra. Her work focuses on Bayesian and adaptive clinical trial design, regulatory alignment, prior specification, prior-data conflict, and operating characteristics of composed adaptive pipelines... Read More →
avatar for Anna Heath

Anna Heath

The Hospital for Sick Children
Dr. Heath is a Scientist at The Hospital for Sick Children (SickKids), Toronto, an Associate Professor at University of Toronto and Honorary Research Fellow at University College London, UK. She is the Canada Research Chair in Statistical Trial Design. She arrived at SickKids in 2018... Read More →
Chairs/Hosts
EE
Organizers
EE
S404 Abstracts pdf
Tuesday October 6, 2026 13:00 - 14:00 UTC
Zoom Room #4
  Invited Session, Technical Statistical Session
  • Session ID 404

13:30 UTC

S115 - Reliable Variable Selection for Biomedical Data Science: From Shrinkage Estimation to Interpretable Learning
Tuesday October 6, 2026 13:30 - 14:00 UTC
Zoom Room #1
Modern biomedical data science increasingly relies on datasets with many predictors, limited sample sizes, and complex correlation structures. In these settings, classical regression and standard variable-selection methods may lead to unstable models, overfitting, or conclusions that are difficult to interpret. Shrinkage and penalized estimation provide a principled framework for improving reliability, but their success depends on how multicollinearity and dependence among predictors are handled. This talk discusses reliable variable selection for biomedical data science, moving from classical shrinkage ideas to modern interpretable learning. The presentation will introduce the motivation behind ridge-type methods, LASSO-based procedures, elastic net estimation, and adaptive penalization, with emphasis on approaches that use correlation information to improve model stability and interpretability. Motivated by biomedical applications such as molecular subtype identification and classification problems, the talk will show how statistically grounded regularization can support both prediction and scientific interpretation. The broader message is that reliable biomedical data science requires methods that are not only accurate, but also stable, transparent, reproducible, and interpretable for domain experts.
Speakers
avatar for Mina Norouzirad

Mina Norouzirad

Center for Mathematics and Applications (NOVA Math) and Department of Mathematics, NOVA FCT, Portugal
Mina Norouzirad is an Assistant Researcher in Statistics at the Department of Mathematics and the Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, NOVA University Lisbon, Portugal. She is also Co-Coordinator of the Data Science Thematic Line... Read More →
Organizers
avatar for Mina Norouzirad

Mina Norouzirad

Center for Mathematics and Applications (NOVA Math) and Department of Mathematics, NOVA FCT, Portugal
Mina Norouzirad is an Assistant Researcher in Statistics at the Department of Mathematics and the Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, NOVA University Lisbon, Portugal. She is also Co-Coordinator of the Data Science Thematic Line... Read More →
Tuesday October 6, 2026 13:30 - 14:00 UTC
Zoom Room #1
  Plenary Talk, Technical Statistical Session

14:00 UTC

S116 - Data, Equity and Power: Institutional Frameworks for Embedding Young African Women in Decision-Making
Tuesday October 6, 2026 14:00 - 14:30 UTC
Zoom Room #1
Despite advances in data science and statistical training across Africa, a structural disconnect persists between the development of technical talent and its integration into national and regional policy processes. It is particularly pronounced for early-career African women statisticians, who face intersecting institutional barriers,UN Women and PARIS21 shows that women occupy only 15% of chief statistician positions in Sub-Saharan Africa, reflecting patriarchal institutional cultures, limited senior-level mentorship and rigid career progression pathways.

This abstract proposes an actionable policy framework to transition young African women statisticians from technical implementers to strategic policy influencers. Using a comparative case-study methodology through the Young African Statisticians Association network, we examine institutional barriers and entry pathways across selected National Statistical Offices in East and West Africa.

The framework advances three strategic pillars: institutional quotas and fast-track leadership pathways for young women in state-led data initiatives; coordinated mentorship systems linking global statisticians with local networks to institutionalize peer and senior sponsorship and gender-responsive data governance through dedicated advisory positions for young women statisticians in ministerial policy processes.

Embedding young women statisticians in Africa's governance is vital for equitable, evidence-based development.
Speakers
avatar for Sarah Nzioka

Sarah Nzioka

MEL Manager, RefugePoint
A results-oriented MEL Management professional with 8+ years of progressive multi-sector experience in monitoring, evaluation, and managing complex project lifecycles from inception to completion. Possessing proven expertise in designing and implementing tailored M&E & research frameworks... Read More →
Organizers
avatar for Sarah Nzioka

Sarah Nzioka

MEL Manager, RefugePoint
A results-oriented MEL Management professional with 8+ years of progressive multi-sector experience in monitoring, evaluation, and managing complex project lifecycles from inception to completion. Possessing proven expertise in designing and implementing tailored M&E & research frameworks... Read More →
Tuesday October 6, 2026 14:00 - 14:30 UTC
Zoom Room #1
  Plenary Talk, Career or Professional Development Session

14:00 UTC

S503 - Selected Topics in Statistics
Tuesday October 6, 2026 14:00 - 15:00 UTC
Zoom Room #5
A Bayesian satellite turbidity calibration algorithm framework with a focus on the Great Barrier Reef, Australia | A Statistical Assessment of Urban–Rural Differences in HIV Outcomes Following ART Introduction in Kenya, 2003–2008 | Leveraging Forum Theatre to Enhance Communication and Confidence in Biostatistical Practice | Motivating young adults to pursue (and hold) the careers of their choice
Speakers
avatar for Tanya Sun

Tanya Sun

UC Berkeley
Tanya Sun is a student at the University of California, Berkeley, pursuing a BA in statistics and applied mathematics. Her research focuses on health disparities, infectious disease epidemiology, and the application of statistical methods to population health questions. Tanya is interested... Read More →
avatar for Adriana Perez

Adriana Perez

The University of Texas Health Science Center at Houston
Professor at the Department of Biostatistics and Data Science. Dr. Pérez is engaged in a wide range of research projects: theoretical model evaluation accounting for imputation uncertainty; fitting complex data; analysis of cluster-randomized community trials; clinical trials; analysis... Read More →
avatar for Amanda Young

Amanda Young

Geisinger
Amanda Young, MS, is Director of the Biostatistics Core at Geisinger, where she leads a team of MS- and PhD-level biostatisticians supporting clinical, epidemiologic, and translational research across the clinical enterprise and the School of Health Sciences. She brings deep expertise... Read More →
avatar for Haritini Tsangari

Haritini Tsangari

University of Nicosia
Haritini Tsangari is a Professor in the School of Business at the University of Nicosia, Cyprus. She has a BSc in Mathematics and Statistics (University of Cyprus), an MSc and a PhD in Statistics (Pennsylvania State University, USA). She has published more than 150 high-impact journal... Read More →
Organizers
avatar for IDWSDS Program Committee
S503 Abstracts pdf
Tuesday October 6, 2026 14:00 - 15:00 UTC
Zoom Room #5
  Contributed Session, Other, Career or Professional Development Session, Technical Statistical Session

14:00 UTC

S212 - Career Perspectives from Industry in the U.S.
Tuesday October 6, 2026 14:00 - 15:00 UTC
Zoom Room #2
This session will feature three 10-minute presentations from women working across industry sectors, including transportation, pharmaceuticals, and government research. Speakers will share insights into their career paths, current roles, and day-to-day responsibilities. The presentations will be followed by a moderated discussion exploring key topics such as career progression, mentorship, opportunities for growth, and leadership, offering attendees practical perspectives on navigating and advancing in the field of statistics and data science.
Speakers
avatar for Sonali Garg

Sonali Garg

Xilio Therapeutics
Sonali Garg is an accomplished statistical programming leader with nearly 20 years of experience across the pharma, biotech, and CRO industries. She holds a graduate degree in biostatistics and an undergraduate degree in statistics.

Throughout her career, Sonali has guided numerous compounds through complex global submission, approval, and post-approval processes. She has prepared multiple successful submissions to the FDA and EMA, navigated regulatory audits, and worked directly with regulators and senior management... Read More →
avatar for Christian Douglas

Christian Douglas

RTI International
Dr. Christian E. Douglas is a Senior Research Statistician at RTI International with more than 14 years of experience supporting clinical trials and large-scale observational studies. Her expertise includes longitudinal data analysis, adaptive trial design, power and sample size estimation... Read More →
avatar for Ashley Asmus

Ashley Asmus

RSG Inc.
Ashley Asmus is a researcher, project manager, and data scientist with over nine years of experience working with complex datasets, including six years focused on travel survey data in both public and private sectors. Prior to joining RSG Inc., she served as lead researcher and deputy... Read More →
Chairs/Hosts
avatar for Sophie Salem

Sophie Salem

Statistician, RTI International
Sophie Salem is an early career statistician at RTI International. She provides statistical support on various large-scale federally funded surveys, including the National Health and Nutrition Examination Survey and the National Dementia Workforce Study. She has undergraduate research... Read More →
Organizers
avatar for Cynthia Bland

Cynthia Bland

Past Past President, CWS; RTI
I'm the Past Past President of CWS and also a survey statistician and center director at RTI International. I helped start a CWS mentoring initiative called Flash Mentoring and I support our emergent needs mentoring. Both require CWS membership, which is free to students. Please consider... Read More →
Tuesday October 6, 2026 14:00 - 15:00 UTC
Zoom Room #2
  Invited Panel, Career or Professional Development Session

14:00 UTC

S309 - Modern Frontiers in Complex Data Analysis: Scalable Bayesian Methods and Dynamic Online Estimation
Tuesday October 6, 2026 14:00 - 15:00 UTC
Zoom Room #3
This session brings together pioneering research that tackles the dual challenges of complexity and scalability in modern statistics and data science. The presentations feature cutting-edge developments in Bayesian hypothesis testing, discrete choice modeling, and streaming spatial data analysis over complex domains. The session begins with two complementary talks introducing highly efficient, closed-form Bayesian alternatives to classical multivariate and random effects testing frameworks. It then transitions to an innovative Bayesian best-worst choice model that utilizes Integrated Nested Laplace Approximation (INLA) for complex preference data analysis. Concluding the discussion is a state-of-the-art online nonparametric estimation of spatially varying coefficient models for streaming spatial data using dynamic bivariate penalized splines over triangulations. Together, these talks highlight the vital role of advanced computational methodologies, spanning objective Bayes, deterministic approximations, and sequential updating algorithms, in achieving rigorous inference and extracting robust insights from complex data structures across diverse scientific domains.
Speakers
avatar for Mai Dao

Mai Dao

Assistant Professor, Wichita State University
Dr. Mai Dao is an Assistant Professor of Statistics in the Department of Mathematics, Statistics, and Physics at Wichita State University. She obtained her Ph.D. from Texas Tech University under the supervision of Professors Min Wang (The University of Texas at San Antonio) and Souparno... Read More →
avatar for Zhuanzhuan Ma

Zhuanzhuan Ma

The University of Texas Rio Grande Valley
Dr. Zhuanzhuan Ma is an Assistant Professor of Statistics in the School of Mathematical and Statistical Sciences at the University of Texas Rio Grande Valley. She earned her Ph.D. in Statistics from Texas Tech University in 2022. Her research interests include Bayesian statistics... Read More →
avatar for Nadeesha Jayaweera

Nadeesha Jayaweera

Assistant Professor, University of Akron
Dr. Nadeesha Jayaweera is an Assistant Professor in the Department of Statistics at the University of Akron. She received her Ph.D. in Statistics from Texas Tech University in 2022 and previously held a postdoctoral research position at Worcester Polytechnic Institute. Her research... Read More →
avatar for Jingru Mu

Jingru Mu

Kansas State University
Dr. Jingru Mu is an Associate Professor in the Department of Statistics at Kansas State University. Her research focuses on developing flexible and computationally efficient statistical methods for complex, large-scale, and spatio-temporal data. Her interests include nonparametric... Read More →
Chairs/Hosts
avatar for Mai Dao

Mai Dao

Assistant Professor, Wichita State University
Dr. Mai Dao is an Assistant Professor of Statistics in the Department of Mathematics, Statistics, and Physics at Wichita State University. She obtained her Ph.D. from Texas Tech University under the supervision of Professors Min Wang (The University of Texas at San Antonio) and Souparno... Read More →
Organizers
avatar for Mai Dao

Mai Dao

Assistant Professor, Wichita State University
Dr. Mai Dao is an Assistant Professor of Statistics in the Department of Mathematics, Statistics, and Physics at Wichita State University. She obtained her Ph.D. from Texas Tech University under the supervision of Professors Min Wang (The University of Texas at San Antonio) and Souparno... Read More →
S308 Abstracts pdf
Tuesday October 6, 2026 14:00 - 15:00 UTC
Zoom Room #3
  Invited Session, Technical Statistical Session

14:30 UTC

S117 - Privacy Doesn't End at the Match: Querying PPRL Data in Practice
Tuesday October 6, 2026 14:30 - 15:00 UTC
Zoom Room #1
Privacy-preserving record linkage (PPRL) has a rich methodological literature on encoding, matching, and cryptographic guarantees — but comparatively little guidance exists on what happens after the match: how do researchers actually access, query, and analyze the linked data that results? This presentation addresses that gap directly, walking through real-world architectures used by statistical agencies, such as the U.S. Census Bureau's linkage key infrastructure and UK Trusted Research Environments, to control researcher access to linked data. We’ll cover how to handle potential uncertainty when analyzing linked data, such as producing confidence tiers, adjusting thresholds, and a novel approach to clerical review within the PPRL system. We’ll also walk through concrete querying structures such as linkage maps and secure enclaves and will explore examples of queries and their outputs. Attendees will leave with a clearer picture of the access-and-analysis landscape for linked data, practical considerations for designing their own linkage projects, and a better sense of how privacy shapes analysis.
Speakers
avatar for Emily Gentles

Emily Gentles

RTI International
Emily Gentles is an expert in data linkage, including entity resolution and privacy-preserving record linkage (PPRL). Ms. Gentles has researched efficient PPRL methods; worked to develop secure linkage systems; and designed innovative record linkage procedures, including manual review... Read More →
Organizers
avatar for Emily Gentles

Emily Gentles

RTI International
Emily Gentles is an expert in data linkage, including entity resolution and privacy-preserving record linkage (PPRL). Ms. Gentles has researched efficient PPRL methods; worked to develop secure linkage systems; and designed innovative record linkage procedures, including manual review... Read More →
Tuesday October 6, 2026 14:30 - 15:00 UTC
Zoom Room #1
  Plenary Talk, Technical Statistical Session

15:00 UTC

S406 - Selected Topics in AI and Data Science
Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #4
Predictive Modeling for Irregular and Unbalanced Longitudinal Data: Application to Bariatric Surgery Outcomes | Big Data Is Not Neutral: Hidden Biases in Electronic Health Record Research | Spatio-Temporal Air Quality Risk Modelling | Interpretable Machine Learning-Based Variable Selection under Missing Data with the Feasible Solution Algorithm
Speakers
avatar for Kristina Vatcheva

Kristina Vatcheva

United States, University of Texas Rio Grande Valley
Dr. Kristina P. Vatcheva is an Associate Professor in the School of Mathematical and Statistical Sciences at the University of Texas Rio Grande Valley (UTRGV) College of Sciences. She has training and experience in mathematics, computer programming/application development, biostatistics... Read More →
avatar for Sima Sharghi

Sima Sharghi

Akron Children's Hospital
Dr. Sima Sharghi is a Senior Statistician and researcher with expertise in causal inference, Bayesian statistics, real-world evidence, electronic health record analytics, and predictive modeling in healthcare. She holds a PhD in Statistics and has extensive experience collaborating... Read More →
avatar for María Bugallo

María Bugallo

University of A Coruña
María Bugallo is a Juan de la Cierva postdoctoral researcher at the University of A Coruña working in methodological statistics, with a focus on small area estimation and robust statistical modelling. She completed her PhD in Statistics at the Miguel Hernández University of Elche... Read More →
avatar for Maliha Mehnaz Mitu

Maliha Mehnaz Mitu

University of Kentucky
Maliha Mehnaz Mitu is a PhD Candidate in Biostatistics at the University of Kentucky whose research focuses on methodological challenges in interpretable machine learning and Artificial Intelligence for healthcare. Her work integrates statistical learning, missing data methodology... Read More →
Organizers
avatar for IDWSDS Program Committee
S406 Abstracts pdf
Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #4
  Contributed Session, Technical Statistical Session

15:00 UTC

S504 - Additional Topics in Health and Biostatistics
Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #5
Correcting Bias from Covariate-Dependent Censoring in Survival Disparity Decomposition | End-of-Life Healthcare Utilization among Medicare Beneficiaries with Parkinson's Disease- Alzheimer's Disease-Related Dementia at USA Academic Movement Disorders Centers: A 2019 Pre-Intervention Assessment | End-of-Life Healthcare Utilization among Medicare Beneficiaries with Parkinson’s Disease Dementia at USA Academic Movement Disorders Centers: A 2019 Pre-Intervention Assessment | Physical Activity Patterns by Age Among U.S. Adults: Associations with Chronic Conditions, Health Behaviors, and Health-Related Quality of Life
Speakers
avatar for Adriana Perez

Adriana Perez

The University of Texas Health Science Center at Houston
Professor at the Department of Biostatistics and Data Science. Dr. Pérez is engaged in a wide range of research projects: theoretical model evaluation accounting for imputation uncertainty; fitting complex data; analysis of cluster-randomized community trials; clinical trials; analysis... Read More →
avatar for Gloria Oppong

Gloria Oppong

Graduate Student(Research Assistant), university of Tennessee at Chattanooga
Gloria Oppong is an incoming Ph.D. student in Biostatistics at Florida State University. She recently earned her M.S. in Mathematics with a concentration in Applied Statistics from the University of Tennessee at Chattanooga. Her research focuses on public health statistics, epidemiology... Read More →
avatar for Sabrina Zhang

Sabrina Zhang

Westat
Sabrina Zhang has been working as a survey statistician at the statistics and data science department at Westat for over 10 years. At Westat, she works on sampling, weighting, propensity models, data linkage, and other topics across education, health, transportation surveys and so... Read More →
Organizers
avatar for IDWSDS Program Committee
S504 Abstracts pdf
Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #5
  Contributed Session, Technical Statistical Session

15:00 UTC

S118 - Data, Diversity, and Dialogue: Women Shaping the Future of Statistics and Data Science
Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #1
At a time when data drives decisions across science, industry, and society, the voices shaping this landscape matter more than ever. This invited session brings together distinguished women researchers whose work reflects the power of diversity, collaboration, and intellectual curiosity in statistics and data science.

Through their journeys, the speakers will illustrate how meaningful connections—across disciplines, institutions, and cultures—emerge not only from shared research goals but also from openness, mentorship, and resilience. Their experiences reveal how collaborative environments can spark innovation, amplify impact, and create pathways for future generations of women in the field.

More than a showcase of research, this session is an invitation to reflect on how we build scientific communities that are inclusive, dynamic, and forward-looking. By fostering dialogue and embracing diverse perspectives, we strengthen our collective ability to tackle complex challenges and shape a more connected and equitable future for data science.

This session celebrates the role of women as catalysts for change, bridging ideas and people to advance knowledge and inspire the next generation.

This session is sponsored by the Portuguese Statistical Society an by the Portuguese Country Representative of CWS.
Speakers
avatar for Lisete Sousa

Lisete Sousa

Faculdade de Ciências da Universidade de Lisboa and CEAUL
Lisete Sousa is an Associate Professor in the Department of Mathematical Sciences at the Faculty of Sciences of the University of Lisbon and a researcher at the Centre of Statistics and its Applications (CEAUL). She holds a degree in Statistics and Operations Research, a master’s... Read More →
avatar for Conceição Amado

Conceição Amado

Instituto Superior Técnico, Universidade de Lisboa
Conceição Amado is an Associate Professor in the Department of Mathematics at Instituto Superior Técnico, University of Lisbon. She is a researcher at the Center for Computational and Stochastic Mathematics (CEMAT) and serves as co-coordinator of the M.Sc. in Data Science and Engineering... Read More →
avatar for Marília Antunes

Marília Antunes

Faculdade de Ciências da Universidade de Lisboa and CEAUL
Marília Antunes is an Associate Professor at the Department of Mathematical Sciences at the Faculty of Sciences, University of Lisbon. Since 2023, she is also the Scientific Coordinator of the Centre of Statistics and its Applications (CEAUL). She earned her PhD in Statistics and... Read More →
avatar for Susana Vinga

Susana Vinga

INESC-ID, Instituto Superior Técnico and IDMEC
Susana Vinga is Associate Professor at the Departments of Computer Science and Engineering and Bioengineering at Instituto Superior Técnico, Universidade de Lisboa, and researcher at INESC-ID (Life and Health Technologies). She holds a degree in Mechanical Engineering (1999), a post-graduate... Read More →
Chairs/Hosts
EC
Organizers
EC
S118 Abstracts pdf
Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session

15:00 UTC

S213 - Causal Outside the Classroom: How is causal inference used in day-to-day working environments?
Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #2
Causal inference is a term that has become a buzzword and a popular field of research in many fields recently, and arises often in statistics. Causal methodologies can sound overly complex to many collaborators, and others may fixate on the need to have causal results from all possible studies. New researchers and scientists are left with questions of how often to employ these newer methods and frameworks of thinking, when it is appropriate to do so, and what results can actually be drawn from causal analyses.
In this session, we will hear from four professionals who use causal inference in their day-to-day practice. First, Dr. Candice Johnson will discuss whether certain causal studies are inherently gendered by examining hazards in the workforce in male-dominated fields. Dr. Lucy McGowan will talk about whether interaction effects are feasible, clinically significant, and better than subgroup-specific effects in real-world trials. Dr. Maria DeYoreo will explore inferential approaches to estimate causal effects from a study on the effect of state-funded family planning policies on birth outcomes. Finally, Dr. Lu Wang will discuss applying a causal framework to personalized healthcare, demonstrating how causal inference can support flexible, patient-centered decision-making in routine clinical practice.
Speakers
avatar for Lucy D’Agostino McGowan

Lucy D’Agostino McGowan

Associate Professor, Wake Forest University
Lucy D’Agostino McGowan is an associate professor in the Department of Statistical Sciences at Wake Forest University. She received her PhD in Biostatistics from Vanderbilt University and completed her postdoctoral training at Johns Hopkins University Bloomberg School of Public... Read More →
avatar for Candice Johnson

Candice Johnson

Michigan State University
Candice Johnson is Assistant Professor in the Department of Epidemiology and Biostatistics at Michigan State University. She studies how work-related exposures and policies affect the health of workers and their families, with a focus on women and pregnant women in the workforce... Read More →
avatar for Maria DeYoreo

Maria DeYoreo

RAND
Maria DeYoreo is a senior statistician at RAND. Her substantive interests include health care quality and performance measurement, Medicare Advantage, hospice care, and maternal/child health. Her methodological interests include causal inference and quasi-experimental methods, Bayesian... Read More →
avatar for Lu Wang

Lu Wang

University of Michigan
Dr. Lu Wang received her PhD in Biostatistics from Harvard University in 2008 and joined the faculty at the University of Michigan in the same year. Her research focuses on statistical methods for evaluating dynamic treatment regimes, personalized health care, nonparametric and semiparametric... Read More →
Chairs/Hosts
avatar for Ashley Mullan

Ashley Mullan

Vanderbilt University
Ashley Mullan is a PhD student and research assistant in the Department of Biostatistics at Vanderbilt University. She earned her MS in Statistics from Wake Forest University. Her research interests include both methods development for measurement error and missing data and their... Read More →
Organizers
avatar for Jamie Joseph

Jamie Joseph

Henry Ford Health
Dr. Jamie Joseph is a faculty biostatistician in the Department of Public Health Sciences at Henry Ford Health with a joint appointment as an Assistant Professor of Research at Michigan State University. She earned her PhD in Biostatistics from Vanderbilt University in 2024. Currently... Read More →
S213 Abstracts pdf
Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #2
  Invited Session, Technical Statistical Session
  • Session ID 213

15:00 UTC

S310 - Read to Lead and Succeed in Statistics and Data Science
Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #3
In this session, you will learn why it is important for statisticians and data scientists to read books on leadership. You will appreciate how leadership books can inspire statisticians to be better leaders and collaborators. You will be introduced to the perspectives of statisticians about leadership. And you will be challenged to join or start a leadership book club to accelerate your career development.
Speakers
avatar for Ksenija Dumicic

Ksenija Dumicic

University of Zagreb, Faculty of Economics and Business & DOBA University of AS
Ksenija Dumičić, Ph.D., is a tenured full professor in statistics from the University of Zagreb Faculty of Economics & Business and is currently a visiting professor at DOBA University of Applied Sciences in Slovenia. She founded and led Croatia’s first postgraduate study programme... Read More →
avatar for Amanda Golbeck

Amanda Golbeck

College of PUblic Health, University of Arkansas for Medical Sciences
Amanda L. Golbeck is a statistician, academic leader, historian, biographer, and communicator of science. She is known for her authored books, "Equivalence: Elizabeth L. Scott at Berkeley" (Chapman & Hall, 2017) and "Florence Nightingale David: A Passionate Probabilist, Statistician... Read More →
avatar for James Cochran

James Cochran

Professor of Applied Statistics and the Mike and Kathy Mouron Chair, The University of Alabama
Dr. Cochran is Professor of Applied Statistics and the Mike and Kathy Mouron Research Chair with The University of Alabama. He was one of the founders of Statistics without Borders.
avatar for Jessica Utts

Jessica Utts

University of California, Irvine
Jessica Utts is Professor Emerita of Statistics at the University of California, Irvine, where she served as department chair for 5 years. She has held numerous leadership roles in statistics organizations including as President of ASA, the Caucus for Women in Statistics, and WNAR... Read More →
Chairs/Hosts
BN
Organizers
avatar for Amanda Golbeck

Amanda Golbeck

College of PUblic Health, University of Arkansas for Medical Sciences
Amanda L. Golbeck is a statistician, academic leader, historian, biographer, and communicator of science. She is known for her authored books, "Equivalence: Elizabeth L. Scott at Berkeley" (Chapman & Hall, 2017) and "Florence Nightingale David: A Passionate Probabilist, Statistician... Read More →
S309 Abstracts pdf
Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #3
  Invited Session, Career or Professional Development Session

16:00 UTC

K4
Tuesday October 6, 2026 16:00 - 17:00 UTC
Zoom Room #1

Speakers
avatar for Diana Šimić

Diana Šimić

Retired Full Professor

Organizers
avatar for IDWSDS Program Committee
Tuesday October 6, 2026 16:00 - 17:00 UTC
Zoom Room #1
  Keynote Session

17:00 UTC

S119 - Statistical Methods and Applications 3
Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #5
Adaptive Functioning in Angelman Syndrome: Insights from Statistical Modeling | Novel Influence Diagnostics in Multistate Models for Breast Cancer | Goodness-of-fit test for the Dirichlet distribution | A Bayesian Exploration of Bimodal Fertility Patterns Using Lifetime Densities.
Speakers
avatar for Julia Zhang

Julia Zhang

Statistician, RTI International
Julia Ann Zhang graduated from North Carolina State University with a Masters Degree in Statistics and a Concentration in Biostatistics. Currently, she is a statistician at Research Triangle Institute (RTI), where she provides data processing, statistical programming, and data analysis... Read More →
avatar for Valeria Leiva

Valeria Leiva

Publicado Health Department, Universidad de los Andes, Chile
I am a Statistician with B.Sc., M.Sc., and Ph.D. training in Statistics from Pontificia Universidad Católica de Chile. I am currently affiliated with the Department of Public Health at Universidad de los Andes, Chile. My research focuses on joint modeling of longitudinal and survival... Read More →
avatar for Sucharitha Dodamgodage

Sucharitha Dodamgodage

Clarkson University
Sucharitha (Suchi) Dodamgodage is a Biostatistician and Statistics PhD Candidate at Clarkson University who loves turning complex mathematical theory into clean, reliable data pipelines for the life sciences. She solved a major statistical conjecture left open since 1989 regarding... Read More →
avatar for Shambhavi Singh

Shambhavi Singh

Assistant Professor, Statistics, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, India
I am working as an Assistant Professor (Statistics), Applied Sciences Cluster at the School of Advanced Engineering, University of Petroleum and Energy Studies, Uttarakhand, India. I have obtained my Ph.D. in Statistics (2025) from Banaras Hindu University under the supervision of... Read More →
Organizers
avatar for IDWSDS Program Committee
S505 Abstracts pdf
Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #5
  Contributed Session, Technical Statistical Session

17:00 UTC

S407 - Health and Biostatistics 2
Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #4
Scalable Nearest-Neighbor Gaussian Graphical Models for Demographic Heterogeneity in GLP-1 Outcomes|Paired Portfolio Trading: A Statistical Approach|Methodological opportunities in genomic data analysis to advance health equity
Speakers
avatar for Gaoqianxue Liu

Gaoqianxue Liu

Drexel University
Gaoqianxue (Daphne) Liu is a PhD student in Biostatistics at the Dornsife School of Public Health, Drexel University. Her research includes scalable Bayesian methods for high-dimensional and graphical models in electronic health records, longitudinal and trajectory modeling, and machine... Read More →
avatar for Leandra Braeuninger

Leandra Braeuninger

University College London
Leandra Bräuninger (they/them; she/her) is a doctoral student at University College London, supervised by Dr Brieuc Lehmann and Prof. Ioanna Manolopoulou, using statistical and machine learning methods to define, quantify and mitigate genomic inequity. Drawing on algorithmic fairness... Read More →
avatar for Sara Mezuri

Sara Mezuri

Graduate Reasearch Assistant
I am a Ph.D. candidate in the Department of Mathematics and Statistics at Oakland University in Michigan, specializing in Applied Mathematics. Currently, I am in my fifth and final year, and I expect to graduate in December 2026. My research involves developing several statistical... Read More →
Organizers
avatar for IDWSDS Program Committee
S407 Abstracts pdf
Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #4
  Contributed Session, Technical Statistical Session

17:00 UTC

S214 - Beyond Automation: Using Generative AI to Elevate Critical Thinking, Personalization, Engagement, and Analytics Instruction
Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #2
Generative AI is rapidly reshaping the landscape of statistics and business analytics education, offering new opportunities to improve course design, assignment development, and feedback processes. This panel will share practical strategies for integrating AI into teaching workflows while maintaining statistical rigor and fostering student understanding.

We highlight approaches for AI-assisted case and assignment design, including adaptive scenarios in which students pose questions, request instructor-controlled synthetic data, and iteratively refine their analyses. These methods promote deeper engagement with modeling choices, assumptions, and critical thinking-core goals of statistics education.

The panel also demonstrates how AI can support assessment and feedback, from automated classwide test analysis to personalized guidance that helps students interpret results and correct misconceptions.

Attendees will leave with concrete examples, implementation tips, and considerations for responsible use of AI tools in statistics and business analytics classrooms.
Speakers
avatar for George Reckk

George Reckk

Assoc. Prof. of Practice, Babson College
Mr. Recck has taught at Babson College since 1984. Prior to that, Mr. Recck worked his way toward his undergraduate and MBA degrees from Babson while working in Babson's Computer Center. After receiving his MBA from Babson in 1984, Mr. Recck was promoted to Director of Academic Computer... Read More →
avatar for John Draper

John Draper

The Ohio State University
John Draper is an Associate Professor of Clinical Operations and Business Analytics in the Fisher College of Business at The Ohio State University. He has a PhD and MS in statistics from Ohio State as well as BS degrees in mathematics and statistics from Florida State University... Read More →
avatar for Ismael Talke

Ismael Talke

The Ohio State University
Ismael Talke is a Senior Lecturer in the Management Science Department. He joined Fisher in 2019 after serving as a visiting faculty at Miami University Ohio, department of Information Systems and Analytics, where he taught undergraduate Business Analytics, Statistics, and Business... Read More →
avatar for Cheryl Edelmann

Cheryl Edelmann

Senior Lecturer, University of Dayton
Cheryl Edelmann is a professor of Business Analytics in the Department of MIS, OSC, and Business Analytics at the University of Dayton. Cheryl educates future business professionals in the area of statistics, helping them develop the skills needed to succeed in today’s data-driven... Read More →
Chairs/Hosts
avatar for Jessica Kohlschmidt

Jessica Kohlschmidt

Caucus for Women in Statistics and Data Science
Dr. Jessica K. Kohlschmidt is a distinguished biostatistician, educator, and leader in the statistical community. A first-generation college student, her early passion for math and teaching evolved into a dedicated career in statistics after discovering its real-world applications... Read More →
Organizers
avatar for Jessica Kohlschmidt

Jessica Kohlschmidt

Caucus for Women in Statistics and Data Science
Dr. Jessica K. Kohlschmidt is a distinguished biostatistician, educator, and leader in the statistical community. A first-generation college student, her early passion for math and teaching evolved into a dedicated career in statistics after discovering its real-world applications... Read More →
Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #2
  Invited Panel, Career or Professional Development Session

17:00 UTC

S302 - Emerging Computational Methods in Statistical Inference: Multivariate Analysis, Bayesian Shrinkage, and Advanced Regression Modeling
Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #1
This session presents novel computational and methodological advancements in modern statistical inference, focusing on complex data structures such as multivariate, longitudinal, and high-dimensional survival data. The presentations span both frequentist and Bayesian paradigms, featuring innovative strategies to overcome long-standing computational bottlenecks commonly encountered in such investigations. The session opens with a new framework for multivariate order-restricted statistical inference under a general grass ordering, applying a pairwise optimization criterion and introducing an efficient vector projection-based algorithm to estimate the order-restricted mean matrix. It then transitions into the Bayesian realm with two complementary presentations on longitudinal quantile regression. These back-to-back talks first present a robust partially collapsed Gibbs sampler to conquer convergence failures in high dimensions and then showcase a Bayesian adaptive LASSO framework that utilizes clustering-based shrinkage for simultaneous optimal quantile estimation and sparse variable selection. The session concludes by extending advanced continuous shrinkage priors to survival analysis, presenting a hybrid minorize-maximize algorithm for high-dimensional proportional hazards models using the horseshoe+ prior. Together, these talks highlight recent developments of highly efficient algorithms to extract reliable inferences from high-dimensional and complicated datasets.
Speakers
avatar for Mai Dao

Mai Dao

Assistant Professor, Wichita State University
Dr. Mai Dao is an Assistant Professor of Statistics in the Department of Mathematics, Statistics, and Physics at Wichita State University. She obtained her Ph.D. from Texas Tech University under the supervision of Professors Min Wang (The University of Texas at San Antonio) and Souparno... Read More →
avatar for Yujie Jia

Yujie Jia

Wichita State University
Yujie Jia is affiliated with Wichita State University, where she is currently a Ph.D. student in the Mathematics, Statistics, and Physics Department. Her research interests include multivariate order-restricted statistical inference, hypothesis testing, maximum likelihood estimation... Read More →
avatar for Sining Zhang

Sining Zhang

Wichita State University
Sining Zhang is a Ph.D. candidate in Applied Statistics at the Department of Mathematics, Statistics, and Physics at Wichita State University under the supervision of Dr. Mai Dao. Her research focuses on Bayesian quantile regression for longitudinal data, with an emphasis on partially... Read More →
avatar for Sarah Ghazawneh

Sarah Ghazawneh

Wichita State University
Sarah Ghazawneh is a Ph.D. candidate in Applied Mathematics at the Department of Mathematics, Statistics, and Physics at Wichita State University under the supervision of Dr. Mai Dao. Her research focuses on Bayesian quantile regression for longitudinal data, with an emphasis on efficient... Read More →
Chairs/Hosts
avatar for Mai Dao

Mai Dao

Assistant Professor, Wichita State University
Dr. Mai Dao is an Assistant Professor of Statistics in the Department of Mathematics, Statistics, and Physics at Wichita State University. She obtained her Ph.D. from Texas Tech University under the supervision of Professors Min Wang (The University of Texas at San Antonio) and Souparno... Read More →
Organizers
avatar for Mai Dao

Mai Dao

Assistant Professor, Wichita State University
Dr. Mai Dao is an Assistant Professor of Statistics in the Department of Mathematics, Statistics, and Physics at Wichita State University. She obtained her Ph.D. from Texas Tech University under the supervision of Professors Min Wang (The University of Texas at San Antonio) and Souparno... Read More →
S119 Abstracts pdf
Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session

17:00 UTC

S311 - Data Analysis in Engineering and Ecology: Reliability, Branching Processes and Random Fuzzy Sets
Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #3
This session brings together the last advances on Mathematical Statistics, and Data Analysis, considering the stochastic process framework, as well as more general and flexible probability modelling contexts, involving random elements, to represent uncertainly for data processing, in connection with Statistical Inference and Operations Research. Several practical problems can be analyzed, adopting the presented approaches, in Reliability, Engeneering, Social Sciences, Ecology, and Medicine, just to mention a few.

Three invited speakers make up the invited session:

Lucía Bautista Bárcena, University of Extremadura (Spain).
Carmen Minuesa , University of Extremadura (Spain).
Beatriz Sinova Fernández, University of Oviedo (Spain).
Speakers
avatar for Lucía Bautista

Lucía Bautista

Universidad de Extremadura
Lucía Bautista Bárcena is an Assistant Professor of Statistics and Operations Research at the University of Extremadura (Spain). She holds a BSc in Mathematics from the same institution (2017), where she also completed her MSc in Science Research (2019) and her PhD in Mathematics... Read More →
avatar for Carmen Minuesa Abril

Carmen Minuesa Abril

University of Extremadura
Carmen Minuesa is an Associate Professor in the Department of Mathematics and a member of the Institute of Advanced Scientific Computing at the University of Extremadura (UEx). Her research focuses on stochastic processes. She began her career with a PhD on branching processes and... Read More →
avatar for Beatriz Sinova

Beatriz Sinova

University of Oviedo
Beatriz Sinova Fernández is an Associate Professor in the Department of Statistics and Operational Research and Mathematics Didactics at the University of Oviedo. She holds a Bachelor's degree in Mathematics from the University of Oviedo, a Master's degree in Mathematical Modelling... Read More →
Chairs/Hosts
MD
Organizers
MD
S310 Abstracts pdf
Tuesday October 6, 2026 17:00 - 18:00 UTC
Zoom Room #3
  Invited Session, Technical Statistical Session

18:00 UTC

S312 - New advances on Functional data model-based clustering
Tuesday October 6, 2026 18:00 - 18:30 UTC
Zoom Room #3
Functional data analysis has attracted considerable attention in recent years, and its applications appear in physical processes, genetics, biology, meteorology, and signal processing. Many modern applications produce data best viewed as functions rather than finite-dimensional vectors because of their nature. Beyond the challenges of collecting and preprocessing such data, efficiently handling large volumes of functional observations has become an urgent concern. On one hand, functional data takes values in an infinite-dimensional space, which is challenging to handle with classical methods. On the other hand, ignoring the functionality aspect of data will lead to information loss. In this talk, we highlight model-based clustering methods, a powerful tool in machine learning for identifying subgroup-specific patterns. Specifically, we introduce new model-based clustering techniques for functional data, regardless of the Gaussian assumption. The performance of each algorithm is evaluated through simulations and real-world datasets, and the results confirm their efficiency.
Speakers
avatar for Mina Aminghafari

Mina Aminghafari

Associate Professor, University of Calgary
Dr. Mina Aminghafari is an Associate Professor in the Department of Mathematics and Statistics at the University of Calgary. Her research lies at the intersection of high-dimensional statistics, machine learning, and applied data science, particularly on clustering theory and statistical... Read More →
Organizers
avatar for Mina Aminghafari

Mina Aminghafari

Associate Professor, University of Calgary
Dr. Mina Aminghafari is an Associate Professor in the Department of Mathematics and Statistics at the University of Calgary. Her research lies at the intersection of high-dimensional statistics, machine learning, and applied data science, particularly on clustering theory and statistical... Read More →
Tuesday October 6, 2026 18:00 - 18:30 UTC
Zoom Room #3
  Plenary Talk, Technical Statistical Session

18:00 UTC

S408 - Bridging the Gap: How Practicing Data Scientists Use LLMs in the Wild and What It Means for Data Science Education
Tuesday October 6, 2026 18:00 - 18:30 UTC
Zoom Room #4
Since the widespread availability of generative artificial intelligence (GenAI), particularly Large Language Models (LLMs), fundamental questions have emerged about the future of coding in data science. Some predict that data scientists will no longer need traditional coding skills, while others question whether LLMs might replace data scientists entirely. However, these discussions have largely proceeded without empirical evidence of how practicing data scientists actually use these tools.

This study addresses this gap by surveying trained, practicing data scientists to understand if and how they integrate LLMs into their workflows, particularly for writing and editing code and performing other data science tasks. Building on our recent investigation of data science educators' perspectives on LLMs, this research examines real-world usage patterns among practitioners to bridge the gap between current practice and educational preparation.

Our findings will contribute to the data science community in two critical ways. First, by documenting how data scientists are actually working with LLMs four years after their initial release, we provide actionable insights that allow practitioners to learn and adopt effective strategies for integrating these tools into their work. Second, we inform data science education by evaluating whether current pedagogies adequately prepare students for this evolving landscape.
Speakers
avatar for Tiffany Timbers

Tiffany Timbers

University of British Columbia
Dr. Tiffany Timbers is an Associate Professor of Teaching in the Department of Statistics and Instructor in the Master of Data Science program at the University of British Columbia. She holds a PhD in Neuroscience from UBC and completed postdoctoral research in behavioral and neural... Read More →
Organizers
avatar for Tiffany Timbers

Tiffany Timbers

University of British Columbia
Dr. Tiffany Timbers is an Associate Professor of Teaching in the Department of Statistics and Instructor in the Master of Data Science program at the University of British Columbia. She holds a PhD in Neuroscience from UBC and completed postdoctoral research in behavioral and neural... Read More →
Tuesday October 6, 2026 18:00 - 18:30 UTC
Zoom Room #4
  Plenary Talk, Career or Professional Development Session

18:00 UTC

S120 - Statistical Modeling for Applied Research in Costa Rica
Tuesday October 6, 2026 18:00 - 19:00 UTC
Zoom Room #1
This session brings together four applied studies demonstrating how modern statistical methods can address diverse scientific questions using complex data from Costa Rica. The presentations cover Bayesian hierarchical modeling, penalized regression, machine learning, and generalized additive models, with applications to infectious disease surveillance, biotechnology awareness, and animal production. Despite spanning different disciplines, the talks share a common emphasis on developing interpretable and robust statistical approaches to better understand complex systems through real-world applications in the Costa Rican context.
Speakers
avatar for Marianne Peña Wüst

Marianne Peña Wüst

University of Costa Rica
Marianne is a senior Statistics student from the University of Costa Rica with a background in competitive mathematics. Currently, she has been a research assistant at the Centro de Investigación en Matemática Pura y Aplicada (CIMPA), where she collaborates with projects related... Read More →
avatar for Shirley Rojas-Salazar

Shirley Rojas-Salazar

University of Costa Rica
Shirley Rojas is a professor at the School of Statistics, University of Costa Rica. She completed her undergraduate and graduate studies in Statistics. She enjoys applying statistical methods to better understand complex biological processes and address questions in agricultural and... Read More →
avatar for Jimena Murillo-Montero

Jimena Murillo-Montero

Boston Scientific
I grew up in Costa Rica, in a family with strong academic roots. From an early age, I was drawn specially to mathematics and biology. In 2017, I began studying Microbiology and Clinical Chemistry at the University of Costa Rica. In 2018 I added Statistics as a second major, but during... Read More →
avatar for Laura Obando Esquivel

Laura Obando Esquivel

Universidad de Costa Rica
Laura Obando Esquivel is a student in the Bachelor's Degree in Statistics at the University of Costa Rica and a researcher at the School of Biology, working with the Plant Biotechnology Laboratory and the Center for Research in Pure and Applied Mathematics at the same institution... Read More →
Chairs/Hosts
SR
Organizers
SR
S120 Abstracts pdf
Tuesday October 6, 2026 18:00 - 19:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session

18:00 UTC

S505 - Code is for Everyone: Showcasing Tools to Support Learners at all Levels
Tuesday October 6, 2026 18:00 - 19:00 UTC
Zoom Room #5
Code is for Everyone: Showcasing Tools to Support Learners at all Levels
Speakers
avatar for Kelly Bodwin

Kelly Bodwin

California Polytechnic State University
Dr. Kelly Bodwin is an Associate Professor of Statistics and Data Science at California Polytechnic State University, San Luis Obispo. Her research interests include cross-disciplinary work in Digital Humanities, including historical social networks and authorship identification... Read More →
avatar for Mine Cetinkaya-Rundel

Mine Cetinkaya-Rundel

Duke University
Dr. Mine Çetinkaya-Rundel is Professor of the Practice and the Director of Undergraduate Studies at the Department of Statistical Science and the Director of First-Year Experience at Duke University. She is also a Developer Educator at Posit, PBC, an open-source data science software... Read More →
avatar for Cara Thompson

Cara Thompson

Data Visualisation Consultant, Building Stories with Data LTD
Dr. Cara Thompson is a data visualisation consultant with an academic background, specialising in helping research teams and data-driven organisations turn their data insights into to clear and compelling visualisations.

After her PhD in Psychology and a spell teaching research methods at Edinburgh Uni, she embarked on a career in psychometrics at the Royal college of Surgeons of Edinburgh. After ten years of helping surgeons and other medical professionals understand complex patterns in exam data... Read More →
Chairs/Hosts
avatar for Jamie Joseph

Jamie Joseph

Henry Ford Health
Dr. Jamie Joseph is a faculty biostatistician in the Department of Public Health Sciences at Henry Ford Health with a joint appointment as an Assistant Professor of Research at Michigan State University. She earned her PhD in Biostatistics from Vanderbilt University in 2024. Currently... Read More →
Organizers
avatar for Ashley Mullan

Ashley Mullan

Vanderbilt University
Ashley Mullan is a PhD student and research assistant in the Department of Biostatistics at Vanderbilt University. She earned her MS in Statistics from Wake Forest University. Her research interests include both methods development for measurement error and missing data and their... Read More →
S506 Abstracts pdf
Tuesday October 6, 2026 18:00 - 19:00 UTC
Zoom Room #5
  Invited Session, Technical Statistical Session

18:30 UTC

S409 - Every Step Counts: A journey through crossroads, turning points and learnings
Tuesday October 6, 2026 18:30 - 19:00 UTC
Zoom Room #4
A career is rarely a straight line. It is shaped by choices, unexpected opportunities, challenges, mentors, setbacks, and the willingness to keep learning along the way. In this talk, I reflect on my journey from studying statistics in India to pursuing a PhD in Biostatistics in the United States and building a career as a statistician in the biopharmaceutical industry. Along the way, my experiences have taken me across academic research, internships, clinical development, statistical methodology, multiple therapeutic areas, mentoring, professional service, and leadership within the statistical community.
Rather than focusing only on milestones, this talk explores the crossroads and turning points behind them—the decisions that changed direction, the opportunities that initially seemed small but became important, and the lessons learned from navigating uncertainty and growth. Drawing from experiences in research, clinical trials, interdisciplinary collaboration, mentoring, and professional engagement, I will share how curiosity, adaptability, relationships, and continuous learning have shaped my development as a statistician. The central message is simple: careers are built one step at a time, and even the steps that do not seem significant in the moment can ultimately help define where we go next.

Speakers
avatar for Arinjita Bhattacharyya

Arinjita Bhattacharyya

Arinjita Bhattacharyya, PhD, is a biostatistician and statistical scientist with nearly a decade of experience across the pharmaceutical industry and academia. Most recently an Associate Principal Scientist in Biostatistics at Merck, she has supported clinical development across oncology... Read More →
Tuesday October 6, 2026 18:30 - 19:00 UTC
Zoom Room #4
  Plenary Talk, Career or Professional Development Session
  • Session ID 409

19:00 UTC

K5
Tuesday October 6, 2026 19:00 - 20:00 UTC
Zoom Room #1

Speakers
CL
Organizers
avatar for IDWSDS Program Committee
Tuesday October 6, 2026 19:00 - 20:00 UTC
Zoom Room #1
  Keynote Session

20:00 UTC

S313 - Education and Leadership 2
Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #3
Understanding the role of non-academic factors in student success: Perspectives from a minority-serving institution | Gender Norms and Economic Decision-Making: Evidence on Risk and Labor Outcomes | Faculty Teaching and Research Productivity and Equity in STEM: Student Grade Gaps in Lower-Division Courses | Elevating Collaborations with Leadership and Hospitality
Speakers
avatar for Sinjini Mitra

Sinjini Mitra

California State University, Fullerton
Dr. Sinjini Mitra is a professor in the Information Systems & Decision Sciences (ISDS) department at the College of Business and Economics, California State University, Fullerton (CSUF). Previously, she was a postdoctoral research fellow at USC's Information Sciences Institute. Dr... Read More →
avatar for Anna Kye

Anna Kye

University of California Irvine
Anna Kye is a Postdoctoral Scholar at UC Irvine, with appointments at the Postsecondary Education Research Institute and the Division of Teaching Excellence and Innovation. She holds a Ph.D. in Research Methodology (Quantitative) from Loyola University Chicago and an M.S. in Statistics... Read More →
avatar for Kristin Gaffney

Kristin Gaffney

University of Arkansas for Medical Sciences
Kristin Gaffney is a collaborative staff biostatistician in the Department of Biostatistics shared between the College of Medicine and the College of Public Health at the University of Arkansas for Medical Sciences. She has a Master of Public Health degree from the University of Nebraska... Read More →
avatar for Orlie Weitzman

Orlie Weitzman

University of Chicago Laboratory Schools
I am a junior at the University of Chicago Laboratory Schools with an interest in behavioral economics, particularly gender and wealth inequality. I have worked as a student researcher for Professor Marianne Bertrand and am currently involved in an ongoing study on the global rise... Read More →
Organizers
avatar for IDWSDS Program Committee
S313 Abstracts pdf
Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #3
  Contributed Session, Technical Statistical Session, Other, Career or Professional Development Session

20:00 UTC

S215 - Frontiers in Statistical Modeling: High-Impact Doctoral Research from Argentina (Fronteras en la Modelización Estadística: Investigaciones Doctorales de Alto Impacto desde Argentina )
Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #2
Statistical training in Argentina is characterized by quantitative rigor and methodological innovation. Postgraduate programs of excellence, like the PhD in Statistics at the Universidad Nacional de Rosario, build this foundation. This session showcases this training through four doctoral theses that propose cutting-edge solutions for high dimensionality, spatial dependence, incomplete data, and outliers.
Attendees will learn about:
Associative Mapping: Verónica Lac Prugent combines Multivariate Elastic Net models to address quantitative and qualitative responses in genetic resources, ensuring the empirical robustness of associations.
Multi-Environment Trials: Julia Angelini addresses the limitations of the Sites Regression (SREG) model by proposing an Expectation-Maximization (EM) based imputation method and robust variants to reduce the impact of outliers.
Spatial Prediction: Mariel Lovatto introduces a semiparametric spatial autoregressive model that captures spatial dependence without relying on strict parametric covariance, resulting in improved predictive performance.
Zero-Inflated Data: María José Llop introduces a robust EM-type estimation approach for the partially linear zero-inflated Poisson (PLZIP) regression model, effectively handling extreme observations in complex relationships.
This panel highlights the level of Argentine science and its capacity to generate statistical knowledge with global impact.
Speakers
avatar for Verónica Patricia Lac Prugent

Verónica Patricia Lac Prugent

National University of Comahue (Universidad Nacional del Comahue), Argentina
She holds a Bachelor's degree in Statistics, a Specialization in Epidemiology, and is a PhD in Statistics. She works as a Professor and researcher in the Department of Statistics at the Faculty of Economics and Administration of the Universidad Nacional del Comahue, where she also... Read More →
avatar for Julia Angelini

Julia Angelini

Facultad de ciencias económicas y estadística, Facultad de Cs. Económicas y Estadística, Universidad Nacional de Rosario
She holds a Bachelor's degree in Statistics, a Specialization in Bioinformatics, and a PhD in Statistics. She serves as a Professor at the Faculty of Economic Sciences and Statistics of the Universidad Nacional de Rosario and as a senior advanced data scientist at NaranjaX. Currently... Read More →
avatar for Mariel Lovatto

Mariel Lovatto

Universidad Nacional del Litoral (UNL) - CONICET
She holds a degree in Mathematics from the Universidad Nacional del Litoral, and a Master's and a PhD in Statistics from the Universidad Nacional de Rosario. She currently holds a postdoctoral fellowship from CONICET and serves as Head Teaching Assistant at the Faculty of Chemical... Read More →
avatar for María José Llop

María José Llop

Facultad de Ingeniería Química, Universidad Nacional del Litoral
She holds a PhD in Statistics from the Universidad Nacional de Rosario and a Bachelor's degree in Applied Mathematics from the Universidad Nacional del Litoral. She is currently a postdoctoral fellow at CONICET; she serves as a Professor at the Faculty of Chemical Engineering and... Read More →
Chairs/Hosts
MI
Organizers
MI
Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #2
  Invited Panel, Technical Statistical Session

20:00 UTC

S121 - Advances in Statistical Methodology for Complex and High-Dimensional Data
Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #1
This invited session showcases recent advances in statistical methodology for analyzing increasingly complex, high-dimensional, and structured data arising in biomedical research, imaging, surveys, and longitudinal studies. The presentations introduce innovative approaches to sufficient dimension reduction, scalable spatial topic modeling for multiplexed imaging, Bayesian small area estimation under informative sampling, and Bayesian semiparametric quantile mixed-effects models for longitudinal data with missing responses. Together, these talks highlight modern statistical techniques that combine methodological rigor with computational efficiency to address emerging challenges in data science, while demonstrating broad applicability across diverse scientific disciplines.
Speakers
avatar for Mai Dao

Mai Dao

Assistant Professor, Wichita State University
Dr. Mai Dao is an Assistant Professor of Statistics in the Department of Mathematics, Statistics, and Physics at Wichita State University. She obtained her Ph.D. from Texas Tech University under the supervision of Professors Min Wang (The University of Texas at San Antonio) and Souparno... Read More →
avatar for Chenlu Ke

Chenlu Ke

Virginia Commonwealth University
Dr. Chenlu Ke is an Associate Professor in the Department of Statistical Sciences and Operations Research at Virginia Commonwealth University. Her research focuses on methodological and computational developments for high- and ultrahigh-dimensional complex data, with applications... Read More →
avatar for Yanghyeon Cho

Yanghyeon Cho

University of Idaho
Yanghyeon Cho is an Assistant Professor in the Department of Mathematics and Statistical Science at the University of Idaho. Her research interests include survey sampling and statistical genetics, with a focus on small area estimation, Mendelian randomization, and multi-omics data... Read More →
avatar for Xiyu Peng

Xiyu Peng

Texas A&M University
Dr. Xiyu Peng is an assistant professor in the Department of Statistics at Texas A&M University. Her research focuses on developing statistical and computational methods for resolving temporal and spatial dynamics in single cell omics data with applications in cancer. She got her... Read More →
Chairs/Hosts
YN
Organizers
YN
S121 Abstracts pdf
Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session

20:00 UTC

S410 - Statistical Inference with Incomplete Data
Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #4
Incomplete data arise in many scientific fields and present major challenges for modern statistical methodology. Missing values, censoring, and selection bias may substantially affect estimation, hypothesis testing, prediction, and the generalizability of scientific findings. Developing statistical methods that appropriately account for these sources of incompleteness is therefore essential for obtaining reliable conclusions. This session brings together recent methodological advances addressing different aspects of statistical inference with incomplete data. Topics include assessing the generalizability of randomized controlled trials through a sensitivity analysis framework for selection bias, evaluating modern imputation methods for recovering the underlying data distribution, independence testing for high-dimensional incomplete data, and nonparametric testing for censored survival data. Together, these contributions provide complementary perspectives on the analysis of incomplete data and demonstrate how principled statistical methodology can improve inference across a broad range of applications.
Speakers
avatar for Rebecca Andridge

Rebecca Andridge

Professor, The Ohio State University
Rebecca Andridge is a Professor of Biostatistics at The Ohio State University College of Public Health. She conducts methodologic work on imputation methods for missing data, primarily when missingness is driven by the missing values themselves (missing not at random), and on measures... Read More →
avatar for Krystyna Grzesiak

Krystyna Grzesiak

University of Wrocław, Faculty of Mathematics and Computer Science
Krystyna Grzesiak is a PhD candidate at the Institute of Mathematics, University of Wrocław. She conducts research on developing machine learning methods for the analysis of proteomic and metabolomic data.
avatar for Bojana Milošević

Bojana Milošević

Faculty of Mathematics, University of Belgrade
Bojana Milošević is an Associate Professor and Head of the Department of Probability and Statistics at the Faculty of Mathematics, University of Belgrade. Her research interests include nonparametric statistics, statistical hypothesis testing, survival analysis, dependence analysis... Read More →
avatar for Anke Steyn

Anke Steyn

North-West University
Anke Steyn is a lecturer at the North-West University in Potchefstroom, South Africa. Her research interests include survival analysis, goodness-of-fit testing and experimental design. She qualified as an actuary and recently completed her doctoral degree in Statistics. Her great... Read More →
Chairs/Hosts
MC
Organizers
MC
S410 Abstracts pdf
Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #4
  Invited Session, Technical Statistical Session
  • Session ID 410

20:00 UTC

S506 - Recent Advances in Sequential Inference
Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #5
Sequential analysis continues to play a central role in modern statistics by enabling data-driven decision making while balancing statistical efficiency, computational cost, and practical constraints. This session highlights recent methodological advances in sequential and multistage inference across a range of contemporary statistical problems. Topics include sequential estimation of distributional overlap with boundary corrections for reliable confidence interval construction, multistage algorithms for efficient data compression under growing dimensions, penalized sequential estimation and variable selection for recurrent event models with high-dimensional covariates, and multistage sampling strategies for constructing minimum-risk confidence regions for multivariate means. Together, these contributions illustrate how sequential methodologies are being extended beyond their classical foundations to address challenges arising from high-dimensional data, adaptive sampling, optimization of statistical risk, and modern computational applications.
Speakers
avatar for Swarnali Banerjee

Swarnali Banerjee

Associate Professor, Director of Data Science Program, Loyola University Chicago
Dr. Swarnali Banerjee is an Associate Professor and Director of Data Science in the Department of Mathematics and Statistics at Loyola University Chicago. She received her Ph.D. in Statistics from the University of Connecticut in 2014. Prior to joining Loyola University in 2016, she... Read More →
avatar for Zhe Wang

Zhe Wang

Denison University
Dr. Zhe Wang is an Assistant Professor in the Department of Data Analytics at Denison University. She earned her B.S. in Statistics from Beijing Normal University and her M.S. and Ph.D. in Statistics from the University of Connecticut. Her research interests include statistical inference... Read More →
avatar for Swathi Venkatesan

Swathi Venkatesan

Fairfield University
Dr. Swathi Venkatesan is an Assistant Professor in the Department of Mathematics at Fairfield University. She earned her PhD in Statistics from the University of Connecticut. Her research interests include sequential estimation and inference, experimental design, and the development... Read More →
avatar for Laura Dumitrescu

Laura Dumitrescu

Fairfield University
Dr. Laura Dumitrescu is an Associate Professor in the Department of Mathematics at Fairfield University, where she has been a faculty member since 2022. Prior to joining Fairfield, she was a member of the Probability and Statistics research group in the School of Mathematics and Statistics... Read More →
Chairs/Hosts
avatar for Swathi Venkatesan

Swathi Venkatesan

Fairfield University
Dr. Swathi Venkatesan is an Assistant Professor in the Department of Mathematics at Fairfield University. She earned her PhD in Statistics from the University of Connecticut. Her research interests include sequential estimation and inference, experimental design, and the development... Read More →
Organizers
avatar for Swathi Venkatesan

Swathi Venkatesan

Fairfield University
Dr. Swathi Venkatesan is an Assistant Professor in the Department of Mathematics at Fairfield University. She earned her PhD in Statistics from the University of Connecticut. Her research interests include sequential estimation and inference, experimental design, and the development... Read More →
S507 Abstracts pdf
Tuesday October 6, 2026 20:00 - 21:00 UTC
Zoom Room #5
  Invited Session, Technical Statistical Session

21:00 UTC

S216 - Statistical Methods and Applications 2
Tuesday October 6, 2026 21:00 - 22:00 UTC
Zoom Room #2
Mixture-based Nonparametric Estimation of Spatial Covariance Functions with Applications to HIV Key Population Size Estimation across Sub-Saharan Africa | Influenza hospitalization tracking using internet search data and ILI surveillance | The Impact of Recent State Staffing Minimum Mandates on Nursing Home Residents|From Raw Wearable Data to Scientific Inference: A Reproducible Pipeline for Processing Commercial Step Count Data in Aging Cohorts
Speakers
avatar for Manushi Siriwardana

Manushi Siriwardana

Penn State
Manushi Siriwardana is a Ph.D. student in Statistics at Penn State. She will be entering her fourth year of the Ph.D. program in Fall 2024. Her research focuses on spatial statistics, public health, and computational statistics. She is interested in developing statistical methodology... Read More →
avatar for Hanqi Shi

Hanqi Shi

Swarthmore College
Hanqi Shi is an undergraduate student at Swarthmore College majoring in Statistics and Computer Science. Her research interests include statistical modeling, machine learning, and interdisciplinary applications.
avatar for Angira Mondal

Angira Mondal

University of Pennsylvania
Angira Mondal is a Senior Statistical Analyst at the Perelman School of Medicine, University of Pennsylvania, where she works in the Policy and Economics of Disability, Aging, and Long Term Care (PEDAL) Lab. Her research focuses on complex healthcare data, including claims analysis... Read More →
avatar for Nannan Bo

Nannan Bo

Duke University; Dartmouth College
Nannan Bo recently completed her Master of Biostatistics at Duke University and will begin her PhD training in the Quantitative Biomedical Sciences program at Dartmouth College in Fall 2026. Her research interests span clinical trial design, Bayesian methods, measurement error, wearable... Read More →
Organizers
avatar for IDWSDS Program Committee
S216 Abstracts pdf
Tuesday October 6, 2026 21:00 - 22:00 UTC
Zoom Room #2
  Contributed Session, Technical Statistical Session

21:00 UTC

S315 - AI and Data Science 3
Tuesday October 6, 2026 21:00 - 22:00 UTC
Zoom Room #3
From Law to Data Governance: Rebuilding from Scratch at the Intersection of Legal Frameworks and AI | AI in Long‑Term Care: Practical Tools for Workflow Efficiency, Documentation Support, and Caregiver Well‑Being | Which one to use, traditional Algorithms or AI? Approaches for Computational Problem-Solving | Optimizing Portfolio Allocation with Unsupervised Machine Learning
Speakers
avatar for Charity Nyamuchengwa

Charity Nyamuchengwa

clarkson university
Charity is an applied AI practitioner focused on improving workflows, documentation, and safety in long‑term care environments. She develops practical, caregiver‑centered tools including an AI‑assisted documentation helper, a medication‑workflow guidance system, and a synthetic... Read More →
avatar for Weijie Pang

Weijie Pang

Wentworth Institute of Technology
Weijie Pang is a tenure-track assistant professor in School of Computing and Data Science at Wentworth Institute of Technology. After received Ph.D. from Worcester Polytechnic Institute and B.S. and M.S. from Beijing University of Technology, Dr. Pang finished the Post-doc program... Read More →
avatar for Daniela Márquez

Daniela Márquez

Universidad Autónoma de Chiapas
I am a lawyer-turned-data-scientist based in Aguascalientes, Mexico. After more than eight years working in Mexico's Federal Judiciary, I pursued an M.Sc. in Data Science, Big Data and Business Analytics at Universidad Complutense de Madrid and am currently completing a B.Sc. in Statistics... Read More →
avatar for Jae Sook Cheong

Jae Sook Cheong

Data Steward with Data Governance
Dr. Jae Sook Cheong is a senior researcher and a data professional. Her works are in the area of data governance, knowledge representation with data analysis, algorithm analysis, and machine learning. With many years of experience across academia, industry, and national R&D institutes... Read More →
Organizers
avatar for IDWSDS Program Committee
S314 Abstracts pdf
Tuesday October 6, 2026 21:00 - 22:00 UTC
Zoom Room #3
  Contributed Session, Career or Professional Development Session, Other

21:00 UTC

S411 - Fireside Chat with CWS Country Representatives: Navigating the Impact of AI in Teaching, Research, and Professional Practice
Tuesday October 6, 2026 21:00 - 22:00 UTC
Zoom Room #4
Artificial Intelligence (AI) is transforming higher education and professional practice at an unprecedented pace, reshaping teaching, research, and ethics while shifting the professional landscape for women and underrepresented groups in data science.
This invited fireside chat brings together CWS country representatives to share insights from diverse global contexts, highlighting how regional infrastructure gaps and regulatory frameworks shape AI adoption. Structured as an interactive, ask-me-anything-style conversation, the session will engage the audience to foster open dialogue.
Through dynamic exchanges, panellists will explore critical tensions in teaching and learning - such as adapting course design and student assessments to an AI-enabled landscape while combatting the threat of professional "deskilling". The discussion will also examine the dual-edged sword of AI in research, balancing efficiency gains in coding and data processing against concerns regarding algorithmic bias, automated plagiarism, and transparency. Moving beyond academia, the session will address how AI influences broader professional practice, focusing on the need for inclusive ethical frameworks and policies that promote equity.
Ultimately, this cross-regional dialogue aims to identify practical strategies for responsible AI use, fostering global awareness of the opportunities and systemic challenges faced by educators, researchers, and professionals in statistics and data science.
Speakers
avatar for Sofía Villar

Sofía Villar

MRC Biostatistics Unit, University of Cambridge, UK
Sofia S. Villar is a Research Professor and Group Leader within the Efficient Study Design theme at the MRC Biostatistics Unit, University of Cambridge, where she also serves as Academic Lead for Equality, Diversity and Inclusion.

Her research bridges optimization, machine lear... Read More →
avatar for Lígia Henriques-Rodrigues

Lígia Henriques-Rodrigues

Department of Mathematical Sciences and CEAUL, Faculty of Sciences, University of Lisbon
Lígia Henriques Rodrigues is an Associate Professor at the Faculty of Sciences of the University of Lisbon. She is a researcher at the Centre of Statistics and Its Applications of the University of Lisbon.

Her research focuses mainly on Extreme Value Statistics, semi-parametric estimation, bias reduction, risk modelling, Bayesian analysis, spatio-temporal models and survival analysis. Her work combines methodological developments in Statistics with applications to real data, particularly... Read More →
avatar for Gabriela Cybis

Gabriela Cybis

Department of Statistics, Federal University of Rio Grande do Sul, Brazil
Gabriela Bettella Cybis is an Associate Professor in the Department of Statistics at the Federal University of Rio Grande do Sul (UFRGS), Brazil, where she is also a faculty member of the Graduate Program in Statistics.

She holds a B.Sc. in Biology and an M.Sc. in Mathematics f... Read More →
avatar for Atinuke Adebanji

Atinuke Adebanji

Purdue University
Atinuke Adebanji is an Associate Professor of Practice- Statistics at Purdue University, Indianapolis.

Her research and engagement activities focuses on applied multivariate methods, statistical modelling of public health outcomes, multidisciplinary statistical collaboration, and promoting statistical literacy. She has published articles on new distributions applied to health data... Read More →
Chairs/Hosts
WL
Organizers
VL
Tuesday October 6, 2026 21:00 - 22:00 UTC
Zoom Room #4
  Invited Panel, Other
  • Session ID 411

21:00 UTC

S122 - Advances from Junior Bayesian Statisticians: Bayesian Models for Complex Networks and Relational Data
Tuesday October 6, 2026 21:00 - 22:00 UTC
Zoom Room #1
The junior section of the International Society of Bayesian Statistics (j-ISBA) is excited to present a session showcasing the work of outstanding early-career women researchers from the United States, Italy, and Germany. This session highlights recent advances in Bayesian methodology for analyzing complex relational and structured data, with a focus on flexible probabilistic modeling, scalable computation, and impactful real-world applications.

The four talks highlight modern Bayesian approaches for the modeling of networks and high-dimensional relational data. They span generative models for graph-structured data grounded in classical Bayesian principles of exchangeability, Bayesian nonparametric models for interaction networks with evolving community structure, latent variable models that account for uncertainty and reporting bias in social network data, and hierarchical tensor factorization methods for large-scale sparse relational arrays. Across these contributions, speakers develop computationally efficient inferential strategies using tools including variational inference, hybrid Monte Carlo algorithms, and scalable latent variable methods. These approaches enable Bayesian analysis in settings involving millions of observations while maintaining interpretable probabilistic models.

Together, these talks demonstrate the innovative methodological, theoretical, and applied contributions of early-career women Bayesian statisticians.
Speakers
avatar for Federica Zoe Ricci

Federica Zoe Ricci

Swarthmore College
Federica is an Assistant Professor of Statistics (tenure-track) at Swarthmore College, a liberal-arts college located near Philadelphia. She received her PhD in Statistics from UC Irvine. She is currently interested in the development of modeling approaches and scalable computational... Read More →
avatar for Louise Alamichel

Louise Alamichel

Bocconi University
Louise is a post-doctoral researcher at Bocconi University in Milan, working with Daniele Durante. Her research is on Bayesian non-parametric mixture models. On one side, she focuses on the inference of network data using these models, and on the other side, on their asymptotic properties... Read More →
avatar for Martina Contisciani

Martina Contisciani

Center for Critical Computational Studies @ Goethe University Frankfurt
Martina Contisciani is a postdoctoral researcher at the Center for Critical Computational Studies (C3S) at Goethe University Frankfurt. Her research focuses on the development of statistical models and algorithms for the study of complex systems, with a particular interest in inferential... Read More →
avatar for Jie Jian

Jie Jian

The University of Chicago
Jie is a postdoctoral scholar at the Data Science Institute at the University of Chicago. Her research develops interpretable probabilistic and machine-learning methods for network and tensor data. These methods are motivated by applications in brain imaging, climate science, international... Read More →
Chairs/Hosts
EL
Organizers
EL
S122 Abstracts pdf
Tuesday October 6, 2026 21:00 - 22:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session
  • Session ID 122

22:00 UTC

S316 - AI and Data Science 4
Tuesday October 6, 2026 22:00 - 23:00 UTC
Zoom Room #3
Bayesian Time-Dynamic Mixed-Effects Modeling of Longitudinal Data | When the Data Goes Quiet: Using HDBSCAN to Reveal Gendered Street Crime Patterns Hidden in Official Crime Data | From Gut to Data: A Replicable Forecasting Framework for Rural Micro-Retailers in Data-Poor Environments|Collective Intelligence Lead to Open Innovations
Speakers
avatar for Nisha Daniel Raju Daniel

Nisha Daniel Raju Daniel

Bayer AG
Dr. Nisha Daniel R is a qualified Dental Surgeon seamlessly bridging the worlds of clinical medicine and advanced digital health informatics. Currently completing her Master of Science in Data Science for Health and Social Care at the University of Edinburgh, her expertise lies at... Read More →
avatar for Makena Grigsby

Makena Grigsby

University of California, Riverside
Makena Grigsby recently earned her M.S. in Statistics from the University of California, Riverside. Her academic work focuses on Bayesian modeling, survival analysis, machine learning, and statistical computing. She is especially interested in developing and applying flexible statistical... Read More →
avatar for Farah Ahmed

Farah Ahmed

HORUS VISTA LABS and OSU
Farah Ahmed is a Senior Analytics and Insights Lead at Equitable Advisors and the founder and Principal Data Scientist at Horus Vista Labs, an applied data science consultancy serving rural and underserved small businesses. Her work spans demand forecasting, causal inference, and... Read More →
Organizers
avatar for IDWSDS Program Committee
S315 Abstracts pdf
Tuesday October 6, 2026 22:00 - 23:00 UTC
Zoom Room #3
  Contributed Session, Technical Statistical Session, Career or Professional Development Session

22:00 UTC

S123 - Borrowing Strength: Statistical Strategies for Imperfect Data
Tuesday October 6, 2026 22:00 - 23:00 UTC
Zoom Room #1
This session highlights how modern biostatistics tackles a shared challenge across very different problems: making valid, efficient inference when the data we have is incomplete, limited, imbalanced, or imperfectly comparable to the question we want to answer. Talks in this session explore how statisticians "borrow strength" – from historical or external data sources, from related but heterogeneous datasets, from carefully constructed causal designs, and from generative models that learn the structure of missingness itself – to push beyond the limitations of any single dataset. Together, these four talks, spanning clinical trials, large-scale survey methodology, high-dimensional prediction, and observational causal inference, showcase the breadth of ideas at the intersection of biostatistics, machine learning, and causal reasoning, and the methodological creativity required to turn imperfect data into trustworthy conclusions.
Speakers
avatar for Xiaoting Chen

Xiaoting Chen

New York University
Xiaoting Chen is a 4th-year Biostatistics PhD candidate at New York University School of Global Public Health. Her research focuses on dynamic borrowing methods for clinical trial design, with an emphasis on frequentist frameworks that combine historical and concurrent control data... Read More →
avatar for Yuyu(Ruby) Chen

Yuyu(Ruby) Chen

New York University
Yuyu(Ruby) Chen recently graduated from NYU School of Global Public Health with PhD in Biostatistics. Her PhD work focused on developing and evaluating advanced missing data imputation frameworks tailored for healthcare research, which covers the aspects including clinical trials... Read More →
avatar for Jianan Zhu

Jianan Zhu

New York University
Jianan is a rising fifth-year PhD student in Biostatistics. Her main research interest is on design-based causal inference. She is interested in observational studies, randomized trials, sensitivity analysis, and their applications in health policy research, infectious disease research... Read More →
avatar for Iris Zhang

Iris Zhang

New York University
Iris is a 4th-year Biostatistics PhD student at New York University School of Global Public Health. Her research focuses on statistical machine learning and high-dimensional inference, with an emphasis on transfer learning, representation learning, and AI-driven methods for healthcare... Read More →
Chairs/Hosts
YM
Organizers
avatar for Xiaoting Chen

Xiaoting Chen

New York University
Xiaoting Chen is a 4th-year Biostatistics PhD candidate at New York University School of Global Public Health. Her research focuses on dynamic borrowing methods for clinical trial design, with an emphasis on frequentist frameworks that combine historical and concurrent control data... Read More →
S123 Abstracts pdf
Tuesday October 6, 2026 22:00 - 23:00 UTC
Zoom Room #1
  Invited Session, Technical Statistical Session

22:00 UTC

S217 - Experiences of Early Career Biostatisticians in Clinical Trial and Data Coordinating Centers
Tuesday October 6, 2026 22:00 - 23:00 UTC
Zoom Room #2
This session will feature four early career researchers working in academic data coordinating and/or clinical trial centers. The speakers will talk about their (collaborative) biostatistician roles and responsibilities in their respective institutions, sharing about their training journeys and lessons learned along the way.
Speakers
avatar for Emily Roberts

Emily Roberts

University of Iowa
Dr. Emily Roberts is an assistant professor of biostatistics at the University of Iowa. She completed her PhD in Biostatistics at the University of Michigan in 2022, where her dissertation focused on Causal Inference Methods and Intermediate Endpoints in Randomized Clinical Trials... Read More →
avatar for Megan McCabe

Megan McCabe

Assistant Professor, Department of Biostatistics, University of Alabama at Birmingham
Dr. Megan McCabe is an Assistant Professor in the Department of Biostatistics at the University of Alabama at Birmingham (UAB), and the Assistant Director for Clinical Trials Development for the DATA coordinating and Collaborative Research Unit (DATA CRU). She graduated with her PhD... Read More →
avatar for Madeline Abbott

Madeline Abbott

Harvard University
I am a Research Associate in the Center for Biostatistics in AIDS Research (CBAR) at Harvard University. I work on clinical trials for the treatment and prevention of HIV and tuberculosis, collaborating closely with clinicians in the International Maternal Pediatric Adolescent AIDS... Read More →
avatar for Margaret Banker

Margaret Banker

Northwestern University
Dr. Margaret Banker is an Assistant Professor of Biostatistics and Informatics in the Department of Preventive Medicine at Northwestern University Feinberg School of Medicine. As a member of the Northwestern University Data Analysis & Coordinating Center (NUDACC), she collaborates... Read More →
Chairs/Hosts
avatar for Emily Roberts

Emily Roberts

University of Iowa
Dr. Emily Roberts is an assistant professor of biostatistics at the University of Iowa. She completed her PhD in Biostatistics at the University of Michigan in 2022, where her dissertation focused on Causal Inference Methods and Intermediate Endpoints in Randomized Clinical Trials... Read More →
Organizers
avatar for Emily Roberts

Emily Roberts

University of Iowa
Dr. Emily Roberts is an assistant professor of biostatistics at the University of Iowa. She completed her PhD in Biostatistics at the University of Michigan in 2022, where her dissertation focused on Causal Inference Methods and Intermediate Endpoints in Randomized Clinical Trials... Read More →
S217 Abstracts pdf
Tuesday October 6, 2026 22:00 - 23:00 UTC
Zoom Room #2
  Invited Session, Career or Professional Development Session

22:00 UTC

S508 - Statistical Methods and Applications in One Health
Tuesday October 6, 2026 22:00 - 23:00 UTC
Zoom Room #5
This session will showcase statistical methods and data-driven approaches for One Health, reflecting the interconnected health of humans, animals, and the environment. Presentations by women graduate students in statistics will highlight innovative methodological and applied research across One Health domains, spanning the development and application of statistical methods for handling complex, high-dimensional, and heterogeneous data, improving prediction and inference, and addressing uncertainty in human, animal, and environmental health systems. The session also provides a platform for emerging researchers to share their work, engage with the statistical community, and foster collaboration and mentorship.
Speakers
avatar for Sonja Friesen

Sonja Friesen

University of Guelph
Sonja graduated from the University of Manitoba with a BSc in Data Science with a minor in Biological Sciences and recently began a Master's in Statistics with a One Health Concentration at the University of Guelph. She enjoys working on any project that combines health care, R programming... Read More →
avatar for Ayisha Cok

Ayisha Cok

University of Guelph
Ayisha N. COK is a Ph.D. student in Statistics at the University of Guelph. Her research focuses on developing statistical methods for high-dimensional biomedical data, with interests in measurement error, genotype imputation, meta-analysis, Bayesian methods, and integrative data... Read More →
avatar for Heshani Mendis

Heshani Mendis

University of Manitoba
Heshani Mendis is an MSc student in Statistics at the University of Manitoba, Winnipeg, Canada. Her research interests lie in statistical machine learning, with a focus on tree-based ensemble methods and flexible splitting criteria for random forests. Her current work introduces a... Read More →
avatar for Nimsara Dissanayaka

Nimsara Dissanayaka

University of Guelph
Nimsara Dissanayaka a PhD student in Applied Statistics at the University of Guelph. Her research focuses on circular data analysis mainly the development of circular regression models, model assessment, and prediction. She is particularly interested in environmental applications... Read More →
Chairs/Hosts
EA

Elif Acar

University of Guelph
Organizers
EA
S508 Abstracts pdf
Tuesday October 6, 2026 22:00 - 23:00 UTC
Zoom Room #5
  Invited Session, Technical Statistical Session

22:00 UTC

S412 - Building Trustworthy Financial Systems with Explainable AI: Applications in Credit Risk Modeling and Financial Inclusion
Tuesday October 6, 2026 22:00 - Wednesday October 7, 2026 18:30 UTC
Zoom Room #4
Artificial intelligence is transforming how financial institutions assess creditworthiness, detect fraud, and expand access to financial services. Yet many of today's most powerful machine learning models remain opaque, making consequential decisions that customers, regulators, and even financial institutions struggle to interpret. As AI adoption accelerates, ensuring transparency, accountability, and fairness has become essential to building public trust and achieving equitable financial outcomes.
Speakers
avatar for Angela Omogbeme

Angela Omogbeme

University of West Georgia
Angela Omogbeme is a financial technology researcher and data analytics professional specializing in artificial intelligence, explainable AI, fraud detection, credit risk modeling, and financial inclusion.

She holds an M.S. in Business Analytics (4.0 GPA) from the University of West Georgia and an MBA from Edinburgh Business School, Heriot-Watt University, UK. Her research has been presented internationally, including at conferences hosted at the University of Oxford ,UK and the University... Read More →
Organizers
avatar for Angela Omogbeme

Angela Omogbeme

University of West Georgia
Angela Omogbeme is a financial technology researcher and data analytics professional specializing in artificial intelligence, explainable AI, fraud detection, credit risk modeling, and financial inclusion.

She holds an M.S. in Business Analytics (4.0 GPA) from the University of West Georgia and an MBA from Edinburgh Business School, Heriot-Watt University, UK. Her research has been presented internationally, including at conferences hosted at the University of Oxford ,UK and the University... Read More →
Tuesday October 6, 2026 22:00 - Wednesday October 7, 2026 18:30 UTC
Zoom Room #4
  Plenary Talk, Career or Professional Development Session

23:00 UTC

K6 - Connecting Cure Models and Women’s Health: Survival Analysis for Uterine Cancer Data
Tuesday October 6, 2026 23:00 - Wednesday October 7, 2026 00:00 UTC
Zoom Room #1
Survival analysis plays a fundamental role in health research, especially when the objective is to understand not only the time until an event occurs, but also the possibility that a proportion of individuals may never experience that event. This
situation is particularly relevant in cancer studies, where long-term survivors may be considered cured or no longer susceptible to recurrence or death related to the disease.

In this talk, I will present statistical modeling approaches based on cure models, with emphasis on their application to uterine cancer data. The discussion is motivated by my work on regression modeling for cure factors using the reparametrized defective generalized Gompertz distribution. This framework allows the estimation of both survival behavior and cure proportions, providing a flexible tool for analyzing time-to-event data in the presence of a cured fraction.

The presentation will highlight how cure models can contribute to women’s health research by identifying factors associated with long-term survival and by offering interpretable measures for clinical and epidemiological studies. By connecting statistical theory, survival analysis, and real health data, this work illustrates how statistical science can support evidence-based understanding of cancer outcomes and strengthen global connections between data science and public health.
Speakers
avatar for Vera Tomazella

Vera Tomazella

Universidade Federal de São Carlos
Professor Vera Lucia Damasceno Tomazella is a Full Professor and Senior Professor at the Federal University of São Carlos (UFSCar), Brazil, affiliated with the Department of Statistics. She holds a degree in Mathematics from the Federal
University of Maranhão, a Master’s degre... Read More →
Organizers
avatar for IDWSDS Program Committee
Tuesday October 6, 2026 23:00 - Wednesday October 7, 2026 00:00 UTC
Zoom Room #1
  Keynote Session
 
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