BEGIN:VCALENDAR
VERSION:2.0
X-WR-CALNAME:idwsds2026
X-WR-CALDESC:Event Calendar
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//Sched.com International Day Of Women in Statistics and Data Science//EN
X-WR-TIMEZONE:UTC
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T000000Z
DTEND:20261006T003000Z
SUMMARY:S101 - Opening Session
DESCRIPTION: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.
CATEGORIES:OPENING SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:6647f5dbc7e50476fdc1294f8714f75c
URL:http://idwsds2026.sched.com/event/6647f5dbc7e50476fdc1294f8714f75c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T003000Z
DTEND:20261006T010000Z
SUMMARY:S102 - Gini-Weighted Risk Control in Adaptive Generative Augmentation
DESCRIPTION: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. \n \n 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. \n \n 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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:d3b5b76a43294135e666acce92fe6b0c
URL:http://idwsds2026.sched.com/event/d3b5b76a43294135e666acce92fe6b0c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T003000Z
DTEND:20261006T010000Z
SUMMARY:S201- Vision-Language Models and the Harms of Covert Sexualization
DESCRIPTION: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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:972f8cbdb9cbc68073387a7c7ae61de4
URL:http://idwsds2026.sched.com/event/972f8cbdb9cbc68073387a7c7ae61de4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T003000Z
DTEND:20261006T010000Z
SUMMARY:S301 - Why and When Targeted Validation Sampling Offers Statistical Efficiency Gains: A Case Study on Healthy Food Access and Disease Outcomes
DESCRIPTION: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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:6c2558b4edfd322978287009ae62d3fd
URL:http://idwsds2026.sched.com/event/6c2558b4edfd322978287009ae62d3fd
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T010000Z
DTEND:20261006T020000Z
SUMMARY:K1 - A Statistician in the Loop: Building a Career in Trustworthy AI
DESCRIPTION: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.
CATEGORIES:KEYNOTE SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:d293752465a215272723821c5b367380
URL:http://idwsds2026.sched.com/event/d293752465a215272723821c5b367380
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T020000Z
DTEND:20261006T030000Z
SUMMARY:S103- Decoding Health: AI and Big Data at the Frontier of Precision Medicine
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:108f731fac733ad81213ef72b5547601
URL:http://idwsds2026.sched.com/event/108f731fac733ad81213ef72b5547601
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T030000Z
DTEND:20261006T040000Z
SUMMARY:S104 - Breaking barriers
DESCRIPTION: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 \n \n Session Title: Breaking barriers \n \n Session Organiser: Alysha De Livera \n \n Session Chair: Ayse Aysin Bilgin \n \n The progression of the talks: \n \n curiosity → identity → contribution → legacy \n aspiration → exploration → contribution → impact \n \n Speakers: \n 1. Jayamini Liyanage\, j.liyanage@latrobe.edu.au\, La Trobe University\, Australia\, (PhD Student) \n 2. Melissa Middleton\, PhD\, GStat\, melissa.middleton@mcri.edu.au\, Murdoch Children’s Research Institute\, Australia\, (Early Career) \n 3. Alysha De Livera\, PhD\, AStat\, A.DeLivera@latrobe.edu.au\, Latrobe University\, Australia\, (Mid Career) \n 4. Ayse Aysin Bilgin\, PhD\, ayse.bilgin@mq.edu.au\, Macquarie University\, Australia Professor (Late Career)
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:d8f24a46f7bf5f2cf5aa43cd895a057d
URL:http://idwsds2026.sched.com/event/d8f24a46f7bf5f2cf5aa43cd895a057d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T040000Z
DTEND:20261006T050000Z
SUMMARY:S203 - AI and Data Science 1
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:293bf423c007e18f5166f81b3f60e98f
URL:http://idwsds2026.sched.com/event/293bf423c007e18f5166f81b3f60e98f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T040000Z
DTEND:20261006T050000Z
SUMMARY:S105 - Towards Inclusive Medical AI: Mitigating Data Bias and Fostering Equity
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:b9d8a8c7276516081dee9129d5c8703f
URL:http://idwsds2026.sched.com/event/b9d8a8c7276516081dee9129d5c8703f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T050000Z
DTEND:20261006T060000Z
SUMMARY:S106 - Statistical Methods and Applications 1
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:d42b1ca698166c619a53a22ce1c36343
URL:http://idwsds2026.sched.com/event/d42b1ca698166c619a53a22ce1c36343
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T060000Z
DTEND:20261006T070000Z
SUMMARY:K2 - Bayes in Practice: A Bayesian Cancer Atlas
DESCRIPTION: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! \nSelected References \n\nBaade P\, K Mengersen [2024] Building HOPE through the Australian Cancer Atlas Insight+ MJA 35 &nbsp\;//insightplus.mja.com.au/2024/35 \n\nJ 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. \n\nBretherton A\, Bon J\, Warne D\, Mengersen K\, Drovandi C\, [2026] A Principled Approach to Bayesian Transfer Learning\, Bayesian Analysis. To appear. \n\nCramb 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 \n\nGoodwin S\, &nbsp\;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. &nbsp\;JAMIA. Journal of the American Medical Informatics Association\, //doi.org/10.1093/jamia/ocae212 \n\nHassan\, C [2024] Structured Models and Algorithms for Sensitive Data. PhD Thesis\, Queensland University of Technology\, Australia. \n\nHogg J\, J Cameron\, S Cramb\, P Baade\, K Mengersen [2024] &nbsp\;A Two‐stage Bayesian Small Area Estimation Approach for Proportions. &nbsp\;International Statistical Review. V92 I3 455482. \n\nHyland-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 \n\nJahan 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) \n\nLeontyeva 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 \n\nPrice A\, M Rigby\, P Fiévez\, K Mengersen [2025] A spatial vulnerability index for environmental health Ecological Indicators Vol 178\, September 2025\, 113793&nbsp\;
CATEGORIES:KEYNOTE SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:5d82c6e7f69a6ba7fd99e5c368a910a8
URL:http://idwsds2026.sched.com/event/5d82c6e7f69a6ba7fd99e5c368a910a8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T070000Z
DTEND:20261006T080000Z
SUMMARY:S303 - AI and Data Science 2
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:f24095aa8b1c0ba3e9a9f526f33a6a06
URL:http://idwsds2026.sched.com/event/f24095aa8b1c0ba3e9a9f526f33a6a06
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T070000Z
DTEND:20261006T073000Z
SUMMARY:S107 - Absorbing Markov Chain Parameter Estimation Under Data Scarcity: A Comparative Study of Analytical and Monte Carlo Methods in Neonatal Care
DESCRIPTION: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? \n \n 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. \n \n 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. \n \n Keywords: Absorbing Markov Chain\, Monte Carlo Simulation\, Analytical Estimation\, Absorption Probability\, Expected Time to Absorption\, Sensitivity Analysis. \n \n Authors: Emmanuella Frimpong\, Dr. Irene Kafui Vorsah Amponsah\, PhD (Visiting Lecturer at Ohio University)
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:05eec9ba9d5ba8975c3b9afe91f4c010
URL:http://idwsds2026.sched.com/event/05eec9ba9d5ba8975c3b9afe91f4c010
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T070000Z
DTEND:20261006T073000Z
SUMMARY:S204 - Creating Global Connections Through Early Statistical Leadership in Rare Disease Trials
DESCRIPTION: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. \n \n 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. \n \n 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. \n \n The broader message is that statistics is not only an analysis discipline\; it is also a design and connecting discipline.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:3649041bdfe60db6c45c395fc67479ff
URL:http://idwsds2026.sched.com/event/3649041bdfe60db6c45c395fc67479ff
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T073000Z
DTEND:20261006T080000Z
SUMMARY:S108 - Explainable AI in Health Technology
DESCRIPTION: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. \n 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. \n Finally\, the talk will navigate the evolving regulatory landscape\, specifically the EU AI Act.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:b484c5abc6c2cd32705fd8866c97d886
URL:http://idwsds2026.sched.com/event/b484c5abc6c2cd32705fd8866c97d886
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T080000Z
DTEND:20261006T090000Z
SUMMARY:S304 - Education and Leadership 1
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:b34fd8b1c66552000e15d4e6ae3c4570
URL:http://idwsds2026.sched.com/event/b34fd8b1c66552000e15d4e6ae3c4570
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T080000Z
DTEND:20261006T090000Z
SUMMARY:S109 - Advances in Statistical Methods for Complex and High-Dimensional Data
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:1acd3df961e296f05c87b9d31398f92a
URL:http://idwsds2026.sched.com/event/1acd3df961e296f05c87b9d31398f92a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T080000Z
DTEND:20261006T090000Z
SUMMARY:S205 - Time-to-event data in biometry: Challenges\, biases and perspectives
DESCRIPTION: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. \n 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. \n 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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:ba1457db189434c0bbde65b02e796c07
URL:http://idwsds2026.sched.com/event/ba1457db189434c0bbde65b02e796c07
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T080000Z
DTEND:20261006T090000Z
SUMMARY:S400 - Building Future Leaders in Statistics and Data Science Japan's Collaborative Model from School Education to Society
DESCRIPTION:As artificial intelligence and data-driven decision-making become integral to society\, developing future\nleaders in statistics and data science has become an international priority. Beyond teaching statistical\nmethods or programming skills\, education must cultivate statistical thinking\, inquiry\, evidence-based\nreasoning\, and the ability to connect data with real-world decision-making throughout learners'\neducational journeys.\n\nJapan has been developing an inquiry-based learning framework that progressively connects statistical\nand data science education from primary school to university. This framework encourages learners to\ninvestigate authentic questions using data while strengthening statistical literacy through curriculum\nreform\, inquiry-based learning\, student competitions\, and university education and research. It is\nsupported through collaboration among educational institutions\, professional societies\, public\norganizations\, and non-profit organizations.\n\nThis session introduces four complementary perspectives on this framework. First\, it presents how\nlong-term educational support by a non-profit organization has contributed to nurturing future\nstatistical talent. Second\, it explores how national and international statistical competitions\, including the\nStatistical Graph Contest and the ISLP International Poster Competition\, inspire students and foster\nstatistical thinking. Third\, it shares innovative practices in inquiry-based data science education in\nJapanese secondary schools. Finally\, it discusses how statistics and data science education can prepare\nhybrid talent capable of integrating disciplinary expertise with AI and emerging technologies across\nsecondary education\, higher education\, and lifelong learning.\n\nThrough these perspectives\, the session aims to share Japan's inquiry-based educational framework\nwith the international community and to promote dialogue on how statistics and data science education\ncan cultivate future leaders capable of addressing increasingly complex societal challenges.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:7e2b0757fd005cdc206aa0213359b2b1
URL:http://idwsds2026.sched.com/event/7e2b0757fd005cdc206aa0213359b2b1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T090000Z
DTEND:20261006T100000Z
SUMMARY:S401 - Health and Biostatistics 1
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:77a5f64960eef63a25ae00b2dba7221e
URL:http://idwsds2026.sched.com/event/77a5f64960eef63a25ae00b2dba7221e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T090000Z
DTEND:20261006T100000Z
SUMMARY:S110 - Advances in Multi-Source Statistics
DESCRIPTION:This session is organized by the European Survey Research Association – Special Interest Group on multi-source statistics. \n 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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:bced08b83abcd3f70c92b2ad7984022e
URL:http://idwsds2026.sched.com/event/bced08b83abcd3f70c92b2ad7984022e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T090000Z
DTEND:20261006T100000Z
SUMMARY:S305 - The Role of Data Modelling for Better Outcomes in Women’s Health
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:7b6a43d205be1b2a58f5c5bb77d383d4
URL:http://idwsds2026.sched.com/event/7b6a43d205be1b2a58f5c5bb77d383d4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T090000Z
DTEND:20261006T093000Z
SUMMARY:S206 - Beyond Simulation: Benchmarking for Method Comparison in Statistics and Machine Learning
DESCRIPTION: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. \n \n 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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:82458529a51dd1f90f4ca0edf0f68717
URL:http://idwsds2026.sched.com/event/82458529a51dd1f90f4ca0edf0f68717
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T093000Z
DTEND:20261006T100000Z
SUMMARY:S207 - On similarity-based models for Bayesian disease mapping
DESCRIPTION: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). \n 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. \n 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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:cb44663ded3e77f391b14c32db4b6424
URL:http://idwsds2026.sched.com/event/cb44663ded3e77f391b14c32db4b6424
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T100000Z
DTEND:20261006T120000Z
SUMMARY:S402 - Selected Topics in Statistical Methods and Applications
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:2ddbdbff8cd8ae6efa8804568d8e4d22
URL:http://idwsds2026.sched.com/event/2ddbdbff8cd8ae6efa8804568d8e4d22
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T100000Z
DTEND:20261006T110000Z
SUMMARY:S111- Strengthening Statistical Practice Through Collaboration: Perspectives from Women Leaders in STRATOS
DESCRIPTION: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. \n 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. \n 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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:ee3f9ef8f0f32fb7ebba91d3bc2bb7b3
URL:http://idwsds2026.sched.com/event/ee3f9ef8f0f32fb7ebba91d3bc2bb7b3
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T100000Z
DTEND:20261006T110000Z
SUMMARY:S306 - Modern Statistical Methods and Applications in Health Research
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:79e49a8b1606528cbe8fb22ff6ba9367
URL:http://idwsds2026.sched.com/event/79e49a8b1606528cbe8fb22ff6ba9367
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T100000Z
DTEND:20261006T103000Z
SUMMARY:S208 - Optimal Model selection for incidence of Birth Asphyxia: NICU Centers in Greater Accra Region.
DESCRIPTION:Abstract \n 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) \n 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. \n Keywords: Birth asphyxia\, Quasi-Poisson regression\, Gestational age\, Neonatal outcomes\, Maternal health\, Ghana. \n Authors: \n 1. Selina Dadzie (Mphil) \n 2. Irene Kafui Vorsah Amponsah (PhD)
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:5b0a7b4c42370e8c041ca70a5454fb65
URL:http://idwsds2026.sched.com/event/5b0a7b4c42370e8c041ca70a5454fb65
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T103000Z
DTEND:20261006T110000Z
SUMMARY:S209 - Temporal and Regional Variations of Effects of Daily Temperature on Annual Precipitation: A Functional Mixed Effect Model Approach
DESCRIPTION:"Given the increasing threat of global warming\, it is important to understand not only\n global weather patterns but also regional variations throughout countries. Functional\n Linear Mixed-effects Model (FLMM)\, an emerging statistical tool\, provides a comprehensive\n framework for analyzing functional data (e.g.\, data viewed as a function\n or curve) with repeated observations\, allowing researchers to identify patterns and relationships\n in the data. This study applies the functional linear mixed-effects model\n (FLMM) to recognize the effects of temporal and regional variations of short-time\n weather projection (monthly precipitation on daily temperature). We deployed FLMM\n using the daily temperature and monthly precipitation of nine weather stations(Dhaka\,\n Chattogram (Patenga)\, Chattogram (Ambagan)\, Rajshahi\, Khulna\, Barisal\, Sylhet\,\n Rangpur and Mymensingh) of Bangladesh where each station shares the common\n population effects with their individual scalar covariate effects along with same slope\n functions. To estimate variance parameters and fixed-effects and random effects\, the\n REML-based EM algorithm proposed by has been applied. Empirical\n results show significant differences in the effects of monthly precipitation on daily\n temperature among regions of Bangladesh. We anticipated that FLMM is an emerging\n model that can reveal the effects of monthly precipitation and the pace of daily\n temperature fluctuations over time."
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:f91f4ebb08d67ee64032191c25dd0b95
URL:http://idwsds2026.sched.com/event/f91f4ebb08d67ee64032191c25dd0b95
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T110000Z
DTEND:20261006T120000Z
SUMMARY:K3
DESCRIPTION:\n
CATEGORIES:KEYNOTE SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:1cbfd1eb5f50d17ca89795e25fd11a46
URL:http://idwsds2026.sched.com/event/1cbfd1eb5f50d17ca89795e25fd11a46
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T120000Z
DTEND:20261006T130000Z
SUMMARY:S501 - Additional Topics in AI and Data Science 1
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #5\, Online via Zoom
SEQUENCE:0
UID:a3e37f2c211a40bc3d9134c2c254135c
URL:http://idwsds2026.sched.com/event/a3e37f2c211a40bc3d9134c2c254135c
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T120000Z
DTEND:20261006T130000Z
SUMMARY:S307 - Creating Alliances through a Data Feminism Community of Practice
DESCRIPTION: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.
CATEGORIES:INVITED PANEL
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:1e2ee61c57e067b2da009f2c356907d0
URL:http://idwsds2026.sched.com/event/1e2ee61c57e067b2da009f2c356907d0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T120000Z
DTEND:20261006T130000Z
SUMMARY:S210 - Digitalisation of public institutions in low resource settings.
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:47d1bbf7928f0195a5536e52c11af21d
URL:http://idwsds2026.sched.com/event/47d1bbf7928f0195a5536e52c11af21d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T120000Z
DTEND:20261006T130000Z
SUMMARY:S403 - Change point detection and its applications
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:22a9defaf7f496620df3c678b43ffdf4
URL:http://idwsds2026.sched.com/event/22a9defaf7f496620df3c678b43ffdf4
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T120000Z
DTEND:20261006T123000Z
SUMMARY:S112 - Predicting the Right Treatment for the Right Patient: An AI-Powered Decision Support Framework Based on Predicted Individual Treatment Effects
DESCRIPTION: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. \n \n 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. \n \n 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. \n \n 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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:78df1c18592ed3a46ee53594d275b054
URL:http://idwsds2026.sched.com/event/78df1c18592ed3a46ee53594d275b054
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T123000Z
DTEND:20261006T130000Z
SUMMARY:S113 - Navigating Data Sharing in Medical Research
DESCRIPTION: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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:fc69cd09c968310afa47f876b7e037c7
URL:http://idwsds2026.sched.com/event/fc69cd09c968310afa47f876b7e037c7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T130000Z
DTEND:20261006T140000Z
SUMMARY:S502 - Additional Topics in AI and Data Science 2
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #5\, Online via Zoom
SEQUENCE:0
UID:d0d88389480c3eb7e646742ea0107e5a
URL:http://idwsds2026.sched.com/event/d0d88389480c3eb7e646742ea0107e5a
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T130000Z
DTEND:20261006T140000Z
SUMMARY:S211 - Statistical applications across various domains from India
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:83e83cf5c390367011cf164b0e618036
URL:http://idwsds2026.sched.com/event/83e83cf5c390367011cf164b0e618036
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T130000Z
DTEND:20261006T140000Z
SUMMARY:S308 - Pedagogy with Purpose: Tailoring your Statistics Classroom for Specific Audiences
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:5c0006255efe93fac0ed043f3002bc23
URL:http://idwsds2026.sched.com/event/5c0006255efe93fac0ed043f3002bc23
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T130000Z
DTEND:20261006T140000Z
SUMMARY:S404 - Coordinating Evidence for Better Research Design: Priors\, Assumptions\, and Scientific Judgment
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:1ce056cbd7cf5e1ec6d69171dae07d9f
URL:http://idwsds2026.sched.com/event/1ce056cbd7cf5e1ec6d69171dae07d9f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T130000Z
DTEND:20261006T133000Z
SUMMARY:S114 - Fractional Statistical Models via Operator Theory: A Data-Driven Framework for Aviation Analytics
DESCRIPTION: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. \n 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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:9bc029b243d2812469135ef3c0cc8c7e
URL:http://idwsds2026.sched.com/event/9bc029b243d2812469135ef3c0cc8c7e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T133000Z
DTEND:20261006T140000Z
SUMMARY:S115 - Reliable Variable Selection for Biomedical Data Science: From Shrinkage Estimation to Interpretable Learning
DESCRIPTION: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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:bea8f9b61aed16dd95b4cb395adbd291
URL:http://idwsds2026.sched.com/event/bea8f9b61aed16dd95b4cb395adbd291
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T140000Z
DTEND:20261006T150000Z
SUMMARY:S503 - Selected Topics in Statistics
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #5\, Online via Zoom
SEQUENCE:0
UID:fd9eefccb3ee6a06e5b2b80a29ce25ec
URL:http://idwsds2026.sched.com/event/fd9eefccb3ee6a06e5b2b80a29ce25ec
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T140000Z
DTEND:20261006T150000Z
SUMMARY:S212 - Career Perspectives from Industry in the U.S.
DESCRIPTION: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.
CATEGORIES:INVITED PANEL
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:f1123d7b7c05d9b642408f4e0cb78a9f
URL:http://idwsds2026.sched.com/event/f1123d7b7c05d9b642408f4e0cb78a9f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T140000Z
DTEND:20261006T150000Z
SUMMARY:S309 - Modern Frontiers in Complex Data Analysis: Scalable Bayesian Methods and Dynamic Online Estimation
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:6cd5c6073b694940bedff0919a472bbf
URL:http://idwsds2026.sched.com/event/6cd5c6073b694940bedff0919a472bbf
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T140000Z
DTEND:20261006T143000Z
SUMMARY:S116 - Data\, Equity and Power: Institutional Frameworks for Embedding Young African Women in Decision-Making
DESCRIPTION: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. \n \n 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. \n \n 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. \n \n Embedding young women statisticians in Africa's governance is vital for equitable\, evidence-based development.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:472282af09b41e7365b84192069b8dde
URL:http://idwsds2026.sched.com/event/472282af09b41e7365b84192069b8dde
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T143000Z
DTEND:20261006T150000Z
SUMMARY:S117 - Privacy Doesn't End at the Match: Querying PPRL Data in Practice
DESCRIPTION: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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:1a208fdfa0b48831c10e7a57657d5cb9
URL:http://idwsds2026.sched.com/event/1a208fdfa0b48831c10e7a57657d5cb9
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T150000Z
DTEND:20261006T160000Z
SUMMARY:S406 - Selected Topics in AI and Data Science
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:05bd6e5847ffdb62c8ed024a975ead38
URL:http://idwsds2026.sched.com/event/05bd6e5847ffdb62c8ed024a975ead38
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T150000Z
DTEND:20261006T160000Z
SUMMARY:S504 - Additional Topics in Health and Biostatistics
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #5\, Online via Zoom
SEQUENCE:0
UID:59966ddee8bbad76c37d9c6383975de7
URL:http://idwsds2026.sched.com/event/59966ddee8bbad76c37d9c6383975de7
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T150000Z
DTEND:20261006T160000Z
SUMMARY:S118 - Data\, Diversity\, and Dialogue: Women Shaping the Future of Statistics and Data Science
DESCRIPTION: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. \n \n 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. \n \n 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. \n \n This session celebrates the role of women as catalysts for change\, bridging ideas and people to advance knowledge and inspire the next generation. \n \n This session is sponsored by the Portuguese Statistical Society an by the Portuguese Country Representative of CWS.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:4ce909734056daed56da3b2debb9d5b6
URL:http://idwsds2026.sched.com/event/4ce909734056daed56da3b2debb9d5b6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T150000Z
DTEND:20261006T160000Z
SUMMARY:S213 - Causal Outside the Classroom: How is causal inference used in day-to-day working environments?
DESCRIPTION: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. \n 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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:7777895182a20d55da04c6a74e74c84f
URL:http://idwsds2026.sched.com/event/7777895182a20d55da04c6a74e74c84f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T150000Z
DTEND:20261006T160000Z
SUMMARY:S310 - Read to Lead and Succeed in Statistics and Data Science
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:755e51a526f18b6a3ec53e6a79cc9a8b
URL:http://idwsds2026.sched.com/event/755e51a526f18b6a3ec53e6a79cc9a8b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T160000Z
DTEND:20261006T170000Z
SUMMARY:K4
DESCRIPTION:\n
CATEGORIES:KEYNOTE SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:e4b3012323abb235f5a1e21223a1fb50
URL:http://idwsds2026.sched.com/event/e4b3012323abb235f5a1e21223a1fb50
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T170000Z
DTEND:20261006T180000Z
SUMMARY:S119 - Statistical Methods and Applications 3
DESCRIPTION: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.
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #5\, Online via Zoom
SEQUENCE:0
UID:051f5a0e91e6944c8cc9c9dc5b624a95
URL:http://idwsds2026.sched.com/event/051f5a0e91e6944c8cc9c9dc5b624a95
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T170000Z
DTEND:20261006T180000Z
SUMMARY:S407 - Health and Biostatistics 2
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:32968dd5faa999b9298adadf7b5aa3ab
URL:http://idwsds2026.sched.com/event/32968dd5faa999b9298adadf7b5aa3ab
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T170000Z
DTEND:20261006T180000Z
SUMMARY:S214 - Beyond Automation: Using Generative AI to Elevate Critical Thinking\, Personalization\, Engagement\, and Analytics Instruction
DESCRIPTION: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. \n \n 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. \n \n 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. \n \n Attendees will leave with concrete examples\, implementation tips\, and considerations for responsible use of AI tools in statistics and business analytics classrooms.
CATEGORIES:INVITED PANEL
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:6c86a57159e557afbef7885b3904f285
URL:http://idwsds2026.sched.com/event/6c86a57159e557afbef7885b3904f285
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T170000Z
DTEND:20261006T180000Z
SUMMARY:S302 - Emerging Computational Methods in Statistical Inference: Multivariate Analysis\, Bayesian Shrinkage\, and Advanced Regression Modeling
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:3d5cbc6a2a75b2d96b1e753973b85d61
URL:http://idwsds2026.sched.com/event/3d5cbc6a2a75b2d96b1e753973b85d61
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T170000Z
DTEND:20261006T180000Z
SUMMARY:S311 - Data Analysis in Engineering and Ecology: Reliability\, Branching Processes and Random Fuzzy Sets
DESCRIPTION: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. \n \n Three invited speakers make up the invited session:\n \n Lucía Bautista Bárcena\, University of Extremadura (Spain).\n Carmen Minuesa \, University of Extremadura (Spain).\n Beatriz Sinova Fernández\, University of Oviedo (Spain).
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:83fcc132cd647719042a433de7729dcb
URL:http://idwsds2026.sched.com/event/83fcc132cd647719042a433de7729dcb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T180000Z
DTEND:20261006T190000Z
SUMMARY:S120 - Statistical Modeling for Applied Research in Costa Rica
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:3a76cc753657052ba2fe214a19ced08f
URL:http://idwsds2026.sched.com/event/3a76cc753657052ba2fe214a19ced08f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T180000Z
DTEND:20261006T190000Z
SUMMARY:S505 - Code is for Everyone: Showcasing Tools to Support Learners at all Levels
DESCRIPTION:Code is for Everyone: Showcasing Tools to Support Learners at all Levels
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #5\, Online via Zoom
SEQUENCE:0
UID:742cd5700e921280ed35dbb905ce2425
URL:http://idwsds2026.sched.com/event/742cd5700e921280ed35dbb905ce2425
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T180000Z
DTEND:20261006T183000Z
SUMMARY:S312 - New advances on Functional data model-based clustering
DESCRIPTION: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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:bbd594bc0b7a398b9b82dd890cc26ca8
URL:http://idwsds2026.sched.com/event/bbd594bc0b7a398b9b82dd890cc26ca8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T180000Z
DTEND:20261006T183000Z
SUMMARY:S408 - Bridging the Gap: How Practicing Data Scientists Use LLMs in the Wild and What It Means for Data Science Education
DESCRIPTION: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. \n \n 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. \n \n 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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:7e556a9ff383e1607157d5619093f15b
URL:http://idwsds2026.sched.com/event/7e556a9ff383e1607157d5619093f15b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T183000Z
DTEND:20261006T190000Z
SUMMARY:S409 - Every Step Counts: A journey through crossroads\, turning points and learnings
DESCRIPTION: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.\nRather 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.\n\n
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:9f7ab1d82221e3478a81ba085454d2be
URL:http://idwsds2026.sched.com/event/9f7ab1d82221e3478a81ba085454d2be
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T190000Z
DTEND:20261006T200000Z
SUMMARY:K5
DESCRIPTION:\n
CATEGORIES:KEYNOTE SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:fd08b07b590fbeab7f357686f089e541
URL:http://idwsds2026.sched.com/event/fd08b07b590fbeab7f357686f089e541
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T200000Z
DTEND:20261006T210000Z
SUMMARY:S313 - Education and Leadership 2
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:02bffde8791a93bc89c418229d1d0199
URL:http://idwsds2026.sched.com/event/02bffde8791a93bc89c418229d1d0199
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T200000Z
DTEND:20261006T210000Z
SUMMARY:S215 - Frontiers in Statistical Modeling: High-Impact Doctoral Research from Argentina (Fronteras en la Modelización Estadística: Investigaciones Doctorales de Alto Impacto desde Argentina )
DESCRIPTION: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. \n Attendees will learn about: \n 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. \n 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. \n 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. \n 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. \n This panel highlights the level of Argentine science and its capacity to generate statistical knowledge with global impact.
CATEGORIES:INVITED PANEL
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:70d1c78e0bc4eab5116311137091591d
URL:http://idwsds2026.sched.com/event/70d1c78e0bc4eab5116311137091591d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T200000Z
DTEND:20261006T210000Z
SUMMARY:S121 - Advances in Statistical Methodology for Complex and High-Dimensional Data
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:baa4062777e2a1212a6efad2313070d8
URL:http://idwsds2026.sched.com/event/baa4062777e2a1212a6efad2313070d8
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T200000Z
DTEND:20261006T210000Z
SUMMARY:S410 - Statistical Inference with Incomplete Data
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:0971ddad572ae78da880ebcf081d44c6
URL:http://idwsds2026.sched.com/event/0971ddad572ae78da880ebcf081d44c6
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T200000Z
DTEND:20261006T210000Z
SUMMARY:S506 - Recent Advances in Sequential Inference
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #5\, Online via Zoom
SEQUENCE:0
UID:fa87b04b5bdbc4b5214a2744862eb9cb
URL:http://idwsds2026.sched.com/event/fa87b04b5bdbc4b5214a2744862eb9cb
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T210000Z
DTEND:20261006T220000Z
SUMMARY:S216 - Statistical Methods and Applications 2
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:1e747b25f6d34ca3495004ca25f03d6e
URL:http://idwsds2026.sched.com/event/1e747b25f6d34ca3495004ca25f03d6e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T210000Z
DTEND:20261006T220000Z
SUMMARY:S315 - AI and Data Science 3
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:b119aac226ebe0f1e11d61df4aee61d5
URL:http://idwsds2026.sched.com/event/b119aac226ebe0f1e11d61df4aee61d5
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T210000Z
DTEND:20261006T220000Z
SUMMARY:S411 - Fireside Chat with CWS Country Representatives: Navigating the Impact of AI in Teaching\, Research\, and Professional Practice
DESCRIPTION: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. \n 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. \n 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. \n 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.
CATEGORIES:INVITED PANEL
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:87fec5afa3d555f9cf2d4f004d4d049e
URL:http://idwsds2026.sched.com/event/87fec5afa3d555f9cf2d4f004d4d049e
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T210000Z
DTEND:20261006T220000Z
SUMMARY:S122 - Advances from Junior Bayesian Statisticians: Bayesian Models for Complex Networks and Relational Data
DESCRIPTION: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. \n \n 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. \n \n Together\, these talks demonstrate the innovative methodological\, theoretical\, and applied contributions of early-career women Bayesian statisticians.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:566d2efa15aa128c2ebafa42ba34f9e2
URL:http://idwsds2026.sched.com/event/566d2efa15aa128c2ebafa42ba34f9e2
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T220000Z
DTEND:20261006T230000Z
SUMMARY:S316 - AI and Data Science 4
DESCRIPTION: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
CATEGORIES:CONTRIBUTED SESSION
LOCATION:Zoom Room #3\, Online via Zoom
SEQUENCE:0
UID:78acd01c497296bdc2825067e05e37b0
URL:http://idwsds2026.sched.com/event/78acd01c497296bdc2825067e05e37b0
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T220000Z
DTEND:20261006T230000Z
SUMMARY:S123 - Borrowing Strength: Statistical Strategies for Imperfect Data
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:0df33f98461b496e4f622a61935515b1
URL:http://idwsds2026.sched.com/event/0df33f98461b496e4f622a61935515b1
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T220000Z
DTEND:20261006T230000Z
SUMMARY:S217 - Experiences of Early Career Biostatisticians in Clinical Trial and Data Coordinating Centers
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #2\, Online via Zoom
SEQUENCE:0
UID:e93c4d6bcdd02c7973fdf14758a7391d
URL:http://idwsds2026.sched.com/event/e93c4d6bcdd02c7973fdf14758a7391d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T220000Z
DTEND:20261006T230000Z
SUMMARY:S508 - Statistical Methods and Applications in One Health
DESCRIPTION: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.
CATEGORIES:INVITED SESSION
LOCATION:Zoom Room #5\, Online via Zoom
SEQUENCE:0
UID:e582f5513cbb5e74f895fd8ea497b95d
URL:http://idwsds2026.sched.com/event/e582f5513cbb5e74f895fd8ea497b95d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T220000Z
DTEND:20261007T183000Z
SUMMARY:S412 - Building Trustworthy Financial Systems with Explainable AI: Applications in Credit Risk Modeling and Financial Inclusion
DESCRIPTION: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.
CATEGORIES:PLENARY TALK
LOCATION:Zoom Room #4\, Online via Zoom
SEQUENCE:0
UID:61223e0453f42597025ed8fd545b6449
URL:http://idwsds2026.sched.com/event/61223e0453f42597025ed8fd545b6449
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261006T230000Z
DTEND:20261007T000000Z
SUMMARY:K6 - Connecting Cure Models and Women’s Health: Survival Analysis for Uterine Cancer Data
DESCRIPTION: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\nsituation 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.\n\nIn 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. \n\nThe 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.
CATEGORIES:KEYNOTE SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:cafb81486c2def5de75996aaa05c7387
URL:http://idwsds2026.sched.com/event/cafb81486c2def5de75996aaa05c7387
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260822T021510Z
DTSTART:20261007T000000Z
DTEND:20261007T010000Z
SUMMARY:S124 - Closing Session
DESCRIPTION:In our closing session we will celebrate the 24 hours we spent together in this conference! We will hear from you about sessions that were impactful and engaging\, your “ah ha” moments\, and lessons learned from our speakers.&nbsp\;
CATEGORIES:CLOSING SESSION
LOCATION:Zoom Room #1\, Online via Zoom
SEQUENCE:0
UID:6777e07f7b9fbbebe71bb4fd8663660f
URL:http://idwsds2026.sched.com/event/6777e07f7b9fbbebe71bb4fd8663660f
END:VEVENT
END:VCALENDAR