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.

arrow_back View All Dates

18:00 UTC

S312 - New advances on Functional data model-based clustering

Tuesday October 6, 2026 18:00 - 18:30 UTC

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

Speakers

Mina Aminghafari

Associate Professor, University of Calgary

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

Organizers

Mina Aminghafari

Associate Professor, University of Calgary

Tuesday October 6, 2026 18:00 - 18:30 UTC
Zoom Room #3

Plenary Talk, Technical Statistical Session

Country Canada
Session ID 311

18:00 UTC

S408 - Bridging the Gap: How Practicing Data Scientists Use LLMs in the Wild and What It Means for Data Science Education

Tuesday October 6, 2026 18:00 - 18:30 UTC

Zoom Room #4

Since the widespread availability of generative artificial intelligence (GenAI), particularly Large Language Models (LLMs), fundamental questions have emerged about the future of coding in data science. Some predict that data scientists will no longer need traditional coding skills, while others question whether LLMs might replace data scientists entirely. However, these discussions have largely proceeded without empirical evidence of how practicing data scientists actually use these tools.

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

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

Speakers

Tiffany Timbers

University of British Columbia

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

Organizers

Tiffany Timbers

University of British Columbia

Tuesday October 6, 2026 18:00 - 18:30 UTC
Zoom Room #4

Plenary Talk, Career or Professional Development Session

Country Canada
Session ID 408

22:00 UTC

S508 - Statistical Methods and Applications in One Health

Tuesday October 6, 2026 22:00 - 23:00 UTC

Zoom Room #5

This session will showcase statistical methods and data-driven approaches for One Health, reflecting the interconnected health of humans, animals, and the environment. Presentations by women graduate students in statistics will highlight innovative methodological and applied research across One Health domains, spanning the development and application of statistical methods for handling complex, high-dimensional, and heterogeneous data, improving prediction and inference, and addressing uncertainty in human, animal, and environmental health systems. The session also provides a platform for emerging researchers to share their work, engage with the statistical community, and foster collaboration and mentorship.

Speakers

Sonja Friesen

University of Guelph

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

Ayisha Cok

University of Guelph

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

Heshani Mendis

University of Manitoba

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

Nimsara Dissanayaka

University of Guelph

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

Chairs/Hosts

Elif Acar

University of Guelph

Organizers

Elif Acar

S508 Abstracts pdf

Tuesday October 6, 2026 22:00 - 23:00 UTC
Zoom Room #5

Invited Session, Technical Statistical Session

Country Canada
Session ID 508

18:00 UTC

18:00 UTC

22:00 UTC

Get help with the event