Date: Friday, November 15, 2024
Time: 7:45 a.m.–3:30 p.m. Pacific time
Location: Online
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:
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.
Don’t miss this chance to connect with Canada’s statistical sciences community. You’ll learn about current research and expand your professional network.
REGISTER FOR GENERAL ATTENDANCE
| 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: “The Role of Statistics in Data Science, Machine Learning, and AI” Moderator: Bei Jiang Panellists:
See the panel description below |
| 10:45–11:00 | Break |
| 11:00–12:15 | CANSSI Short Talks
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| 12:15–12:30 | Break |
| 12:30–3:15 | Lightning Talks
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| 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:
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, MICCAI, and IPMI.
The Role of Statistics in Data Science, Machine Learning, and AI
About the Panellists
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About Alexandre Bouchard: Alexandre Bouchard 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. |
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About Linglong Kong: Dr. Linglong Kong is a professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. He holds a Canada Research Chair in Statistical Learning and a Canada CIFAR AI Chair. He is a fellow of the American Statistical Association (ASA) and a fellow of the Alberta Machine Intelligence Institute (AMII). His publication record includes more than 100 peer-reviewed articles in top journals such as AOS, JASA, and JRSSB as well as top conferences such as NeurIPS, ICML, ICDM, AAAI, and IJCAI. Dr. Kong currently serves as associate editor of the Journal of the American Statistical Association, the Annals of Applied Statistics, the Canadian Journal of Statistics, and Statistics and its Interface. Additionally, Dr. Kong was a member of the Executive Committee of the Western North American Region of the International Biometric Society, chair of the ASA Statistical Computing Session program, and chair of the webinar committee. He served as a guest editor of the Canadian Journal of Statistics and Statistics and its Interface, associate editor of the International Journal of Imaging Systems and Technology, guest associate editor of Frontiers of Neurosciences, chair of the ASA Statistical Imaging Session, and member of the Statistics Society of Canada’s Board of Directors. He is interested in functional and neuroimaging data analysis, statistical machine learning, robust statistics and quantile regression, trustworthy machine learning, and artificial intelligence in smart health. |
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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. |
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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). |
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About Hongtu Zhu: See the Keynote Lecture section above. |