International Day Of Women in Statistics and Data Science: Full Schedule

Registration opens August 15th at www.idwsds.org.

You will be able to validate your attendance (Check-In to sessions) using your personal schedule.

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07:00 UTC

Tuesday October 6, 2026 07:00 - 08:00 UTC

Observation-Weight-Based Strategies for Robust Stochastic Gradient Boosting in Genomic Prediction | A journey of pivoting: From Local Research Experiences to Global Data Science Connections | Teaching Statistics in the Age of AI: Beyond Calculation in Health Sciences Education

Speakers

Beatriz Gil Comparado

FCTNOVA Math

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

Cherlynn Dumbura

CeSHHAR

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

Camila Martins

Associate Professor

Camila Bertini Martins is an Associate Professor at the Federal University of São Paulo (UNIFESP), Brazil, where she teaches Biostatistics in health sciences programs. She holds a PhD in Statistics from the University of São Paulo (USP). Her research interests include meta-analysis... Read More →

Organizers

IDWSDS Program Committee

IDWSDS

S302 Abstracts pdf

Tuesday October 6, 2026 07:00 - 08:00 UTC
Zoom Room #3

Contributed Session, Technical Statistical Session, Career or Professional Development Session, Other

Country Portugal, Zimbabwe, Brazil
Session ID 302

09:30 UTC

S207 - On similarity-based models for Bayesian disease mapping

Tuesday October 6, 2026 09:30 - 10:00 UTC

Zoom Room #2

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

Speakers

Helena Baptista

Management Information Centre (MagIC), NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal

Helena Baptista is a highly experienced statistician, researcher, and educator, specializing in applied statistics, forecasting, and time series analysis. She has over 25 years of experience in the pharmaceutical industry, finance, and academia, with a strong background in statistical... Read More →

Organizers

Helena Baptista

Management Information Centre (MagIC), NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal

Tuesday October 6, 2026 09:30 - 10:00 UTC
Zoom Room #2

Plenary Talk, Technical Statistical Session

Country Portugal
Session ID 207

13:30 UTC

S115 - Reliable Variable Selection for Biomedical Data Science: From Shrinkage Estimation to Interpretable Learning

Tuesday October 6, 2026 13:30 - 14:00 UTC

Zoom Room #1

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

Speakers

Mina Norouzirad

Center for Mathematics and Applications (NOVA Math) and Department of Mathematics, NOVA FCT, Portugal

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

Organizers

Mina Norouzirad

Center for Mathematics and Applications (NOVA Math) and Department of Mathematics, NOVA FCT, Portugal

Tuesday October 6, 2026 13:30 - 14:00 UTC
Zoom Room #1

Plenary Talk, Technical Statistical Session

Country Portugal
Session ID 115

15:00 UTC

S118 - Data, Diversity, and Dialogue: Women Shaping the Future of Statistics and Data Science

Tuesday October 6, 2026 15:00 - 16:00 UTC

Zoom Room #1

At a time when data drives decisions across science, industry, and society, the voices shaping this landscape matter more than ever. This invited session brings together distinguished women researchers whose work reflects the power of diversity, collaboration, and intellectual curiosity in statistics and data science.

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

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

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

This session is sponsored by the Portuguese Statistical Society an by the Portuguese Country Representative of CWS.

Speakers

Lisete Sousa

Faculdade de Ciências da Universidade de Lisboa and CEAUL

Lisete Sousa is an Associate Professor in the Department of Mathematical Sciences at the Faculty of Sciences of the University of Lisbon and a researcher at the Centre of Statistics and its Applications (CEAUL). She holds a degree in Statistics and Operations Research, a master’s... Read More →

Conceição Amado

Instituto Superior Técnico, Universidade de Lisboa

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

Marília Antunes

Faculdade de Ciências da Universidade de Lisboa and CEAUL

Marília Antunes is an Associate Professor at the Department of Mathematical Sciences at the Faculty of Sciences, University of Lisbon. Since 2023, she is also the Scientific Coordinator of the Centre of Statistics and its Applications (CEAUL). She earned her PhD in Statistics and... Read More →

Susana Vinga

INESC-ID, Instituto Superior Técnico and IDMEC

Susana Vinga is Associate Professor at the Departments of Computer Science and Engineering and Bioengineering at Instituto Superior Técnico, Universidade de Lisboa, and researcher at INESC-ID (Life and Health Technologies). She holds a degree in Mechanical Engineering (1999), a post-graduate... Read More →

Chairs/Hosts

Eunice Carrasquinha

Organizers

Eunice Carrasquinha

S118 Abstracts pdf

Tuesday October 6, 2026 15:00 - 16:00 UTC
Zoom Room #1

Invited Session, Technical Statistical Session

Country Portugal
Session ID 118

07:00 UTC

09:30 UTC

13:30 UTC

15:00 UTC

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