CANSSI Showcase 2024

The CANSSI Showcase is an annual celebration of the work being done by Canadian statistical sciences researchers, postdoctoral fellows, and students—and a chance to connect with Canada’s statistical sciences community.

CANSSI Showcase 2024 will be held virtually on Friday, November 15, 2024. It will be a wonderful opportunity for you to:

• Hear about the work being done within Canada’s statistical sciences community
• Showcase your research (especially if you are a graduate student, postdoc, or early career faculty member)
• Discover career opportunities
• Gain a better understanding of CANSSI’s activities
• Learn about the different ways CANSSI can support your work

We invite you to join us for a full schedule of exciting events, including a keynote presentation by Hongtu Zhu (University of North Carolina at Chapel Hill), a panel discussion with distinguished Canadian and U.S. panellists, lightning talks by students, postdoctoral fellows, and faculty members, and presentations by CANSSI-funded researchers.

You’ll leave with new inspiration, deeper connections, and a richer understanding of what is happening across Canada.

Whether you are a student, a postdoctoral fellow, or a faculty member, the Showcase offers you an opportunity to present your work to a national audience through a 12-minute online lightning talk. Register as a presenter to save your spot.

Space is limited and presentation slots will be filled on a first-come, first-served basis. We encourage you to register early if you hope to present.

REGISTER AS A PRESENTER

Don’t miss this chance to connect with Canada’s statistical sciences community. You’ll learn about current research and expand your professional network.

CLICK ON THE “RESERVE A SPOT” BUTTON ON THIS PAGE TO REGISTER

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Email [email protected].

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(We will add more details as they become available.)

Time (PST) | Activity

7:45–8:00 | Opening and Welcome: Introduction of Speaker

8:00–9:00 | Keynote Lecture: “Revolutionizing Medical Data Analysis: Uniting AI and Statistics for Breakthroughs and Challenges”

Speaker: Hongtu Zhu (University of North Carolina at Chapel Hill)
See the keynote abstract and speaker bio below

9:00–9:15 | Break

9:15–10:45 | Panel Discussion

Panellists: Alexandre Bouchard (University of British Columbia), Linglong Kong (University of Alberta), Aurélie Labbe (HEC Montréal), Bhramar Mukherjee (Yale School of Public Health), Matthew Schlager (Yale School of Public Health), Hongtu Zhu (University of North Carolina at Chapel Hill). See the panel description below

10:45–11:00 | Break

11:00–12:15 | CANSSI Short Talks

Presenters: Mai Ghannam (University of Ottawa): "Block Maxima methods in heavy-tailed heteroskedastic models", Kehinde Olobatuyi (University of Victoria): “Multi-event Dynamic Capture-Recapture Model for Big Data: Estimating Undetected COVID-19 Cases in British Columbia, Canada”, Alex Sharp (University of British Columbia): Title to come, Rishikesh Yadav (HEC Montréal): “Sparse Spatiotemporal Dynamic Generalized Linear Models for Inference and Prediction of Bike Counts”

12:15–12:30 | Break

12:30–3:15 | Lightning Talks

3:15–3:30 | Wrap-up

Revolutionizing Medical Data Analysis: Uniting AI and Statistics for Breakthroughs and Challenges

Abstract: This talk provides an insightful overview of integrating artificial intelligence (AI) and statistical methods in medical data analysis. It is structured into three key sections:

• Introduction to Medical Image Data Analysis: This section sets the stage by outlining the fundamentals and significance of medical image analysis in healthcare, charting its evolution and current applications.
• State-of-the-Art AI Applications and Statistical Challenges: Here, we explore the impact of AI, particularly deep learning, on medical imaging, and address the accompanying statistical challenges, such as data quality and model interpretability.
• Opportunities for Statisticians: The final section highlights the critical role of statisticians in refining AI applications in medical imaging, focusing on opportunities for advancing algorithmic accuracy and integrating statistical rigour. The talk aims to demonstrate the crucial synergy between AI and statistics in enhancing medical data analysis, emphasizing the evolving challenges and the vital contributions of statisticians in this domain.

About the Keynote Speaker: Dr. Hongtu Zhu is a tenured professor of biostatistics, statistics, radiology, computer science, and genetics at University of North Carolina at Chapel Hill. He was DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing between 2018 and 2020 and was Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center between 2016 and 2018. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He has been an elected Fellow of the American Statistical Association and the Institute of Mathematical Statistics since 2011. He received an established investigator award from Cancer Prevention Research Institute of Texas in 2016 and received the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019. He has published more than 340 papers in top journals including Nature, Science, Cell, Nature Genetics, PNAS, AOS, JASA,Biometrika, and JRSSB, as well as 55+ conference papers in top conferences including NeurIPS, AAAI, KDD, ICDM, ICML,

About the Panellists

About Alexandre Bouchard: Alexandre Bouchard(opens in a new tab) is a Professor of Statistics at the University of British Columbia. He received his PhD in computer science from the University of California, Berkeley. His research focuses on computational Bayesian methods and applications in cancer genomics and phylogenetics.

About Aurélie Labbe: Aurélie Labbe is a professor in the Department of Decision Sciences and holder of the FRQ-IVADO Chair in Data Science. She specializes in large-scale data analysis. With a master’s degree in Statistics from Université de Montréal and a PhD in the same discipline from the University of Waterloo, she has spent over 15 years developing statistical tools for big data with applications in the fields of genomics, neuroscience, and biostatistics. Since joining HEC Montréal in 2016, her research interests have largely focused on the analytical challenges generated by data from intelligent transportation systems. Aurélie Labbe is also active in the community, as she has been appointed scientific co-director of IVADO since October 2023, and director of the StatLab at the Centre de Recherche en Mathématiques (CRM) since July 2023.

About Bhramar Mukherjee: Professor Bhramar Mukherjee is currently appointed as Anna M.R. Lauder Professor of Biostatistics and Professor of Chronic Disease Epidemiology at the Yale School of Public Health (YSPH). Professor Mukherjee serves as the inaugural Senior Associate Dean of Public Health Data Science and Data Equity at YSPH. She holds a secondary appointment in the Department of Statistics and Data Science and is affiliated with the MacMillan Center and the Institute for the Foundations of Data Science. She serves on the Yale Cancer Center Director’s cabinet.

Dr. Mukherjees’s research interests span statistical methods for analyzing electronic health records, gene-environment interaction studies, data integration, data equity, shrinkage estimation, and the analysis of environmental mixtures. Collaboratively, she contributes to areas such as cancer, cardiovascular diseases, reproductive health, exposure science, and environmental epidemiology. With over 390 publications in statistics, biostatistics, medicine, and public health, Professor Mukherjee is globally recognized for her research contributions in integrating genetic, environmental and health outcome data. She has served as the Principal Investigator on methodology grants funded by the National Science Foundation (NSF) and the National Institutes of Health (NIH).

About Hongtu Zhu: See the Keynote Lecture section above.

About Linglong Kong: To come

Host or Publisher Canadian Statistical Sciences Institute