The CANSSI Showcase is an annual celebration of the work being done by CANSSI-supported researchers, postdoctoral fellows, and students across Canada.
CANSSI Showcase 2023 will be held virtually on Friday, November 17. 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 Sallie Ann Keller (U.S. Census Bureau, University of Virginia), 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 an 8-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.
(We will add more details as they become available.)
|7:45–8:00||Opening and Welcome: Introduction of Speaker|
|8:00–9:00||Keynote Lecture: “Evolving a Data Enterprise to Support Relevant, Timely, and Equitable Statistical Products”
Speaker: Sallie Ann Keller (U.S. Census Bureau and University of Virginia)
See the keynote abstract and speaker bio below
|9:15–10:45||Panel Discussion: “The Role of Statistics for Public Good and Good Governance”
Moderator: Meredith Franklin
• Josée Bégin (Statistics Canada)
• F. Jay Breidt (NORC at the University of Chicago)
• Dave Campbell (Carleton University, Bank of Canada)
• Sallie Ann Keller (U.S. Census Bureau)
See the panel description below
|11:00–12:15||CANSSI Short Talks
Moderator: Audrey Béliveau | Presenter bios
1. Antonio Herrera Martin (University of Toronto): “Rare Events in Astronomy with Repeating FRBs”
2. Gracia Dong (University of Toronto | University of Victoria): “Using Capture-Recapture with Data Extracts from Healthcare Records to Estimate Population Sizes of Vulnerable Populations – Applications and Data Quality Issues”
3. Benjamin Bloem-Reddy (University of British Columbia): “Non-parametric Hypothesis Tests for Distributional Group Symmetry”
4. Tianyu Guan (Brock University): “Comparison of Individual Playing Styles in Soccer”
Moderator: Saman Muthukumarana | Presenter bios
1. Alysha Cooper (University of Guelph): “Modelling Benthic Compositions Using Regularized DM Regression”
2. Ander Diaz-Navarro (Ontario Institute for Cancer Research): “In Silico Generation of Synthetic Cancer Genomes Using Deep Learning Algorithms”
3. Arthur Chatton (Université de Montréal): “Personalized Dynamic Super Learning”
4. Carlotta Pacifici (HEC Montréal | University of Bologna): “Dynamic Tail Risk Estimation Using Extreme Value Theory: An Application to the S&P 500 Index”
5. Cong Jiang (Université de Montréal): “Efficient and Doubly Robust Estimation of COVID-19 Vaccine Effectiveness Under the Test-negative Design”
6. Di Meng (Wilfrid Laurier University): “Short Selling Incentives and Contingent Convertible Securities”
7. Harsh Kumar (University of Toronto): “Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental Health”
8. Lara Maleyeff (McGill University): “Bayesian Model Averaging for the Identification of Tailoring Variables in Adaptive Factorial Designs”
9. Luke Hagar (University of Waterloo): “Scalable Power Curves with Targeted Hypercube Sampling”
10. Nikola Surjanovic (University of British Columbia): “Exploration-agnostic Geometric Ergodicity of Non-reversible Parallel Tempering”
11. Richard Yan (Simon Fraser University): “A Generalized Phase I/II Dose Optimization Trial Design with Multi-categorial and Multi-graded Outcomes”
12. Skyepaphora Griffith (Queen’s University): “Spectrogram Smoothing for Estimation of the Evolutionary Power Spectra of Uniformly Modulated Processes”
13. Surani Matharaarachchi (University of Manitoba | Government of Manitoba): “Long COVID Prediction in Manitoba Using Clinical Notes Data: A Machine Learning Approach”
14. Xiaoting Li (University of British Columbia): “Estimation of Conditional Value-at-Risk Using Copulas”
16. Yuan Bian (University of Western Ontario): “A Unified Framework of Analyzing Missing Data and Variable Selection Using Regularized Likelihood”
|3:15–3:30||Meme Contest Winners and Wrap-up
Meme Judges: Rafal Kulik and Léo Raymond-Belzile
Evolving a Data Enterprise to Support Relevant, Timely, and Equitable Statistical Products
Abstract: This is an exciting time to be part of official statistics. There is growing demand for statistical products that traditional surveys alone cannot address. Stakeholders want timelier, more accurate, more granular, and differently structured information about people, places, and the economy than ever before. New data sources and data science innovations allow us to meet those demands. In today’s digital era, massive amounts of data are generated as we go about our daily lives. This volume of data generated every day, through commercial and personal transactions and the management of federal, state, and local programs, continues to grow exponentially. This provides an incredible opportunity to revolutionize how we capture and use data to develop relevant products. Instead of limiting ourselves to the data our surveys produce, we can flip the paradigm to design products based on what data users need. To do this we must integrate our survey data with other data sources. This presentation will share how the U.S. Census Bureau plans to re-envision its data enterprise based on a statistical product–first approach. This approach includes eliciting the purposes and uses our data are to support, collaborating with internal and external data users to develop the products using ALL our data assets, and then embracing varying access modes for statistical product dissemination to support stakeholder needs at all levels of data acumen. The research and enabling technologies to support this journey has begun! This work will modernize and transform our official statistical infrastructure.
About the Keynote SpeakerDr. Sallie Ann Keller is chief scientist and associate director of the U.S. Census Bureau’s Research and Methodology Directorate. She also holds an endowed distinguished professorship in biocomplexity and faculty appointments in the School of Medicine, Department of Public Health Services; School of Engineering and Applied Science, Department of Engineering Systems and Environment; and School of Data Science at the University of Virginia (UVA).
As chief scientist, Keller leads the Research and Methodology Directorate’s research centers, each devoted to domains of investigation important to the future of social and economic statistics. The directorate collaborates with teams across the U.S. Census Bureau and with researchers worldwide to develop innovative scientific solutions and advances to ensure the Census Bureau remains a leader in economic and social measurement.
Keller is a nationally recognized research scientist with expertise in social and decision informatics, statistical underpinnings of data science, and data access and confidentiality. She is a leading voice in creating the science of all data and advancing this research across disciplines to benefit society.
Her prior positions include director of the Social and Decision Analytics Division within UVA’s Biocomplexity Institute and Initiative; professor of statistics and director of the Social and Decision Analytics Laboratory within the Biocomplexity Institute of Virginia Tech; academic vice president and provost at the University of Waterloo; director of the Institute for Defense Analyses Science and Technology Policy Institute; the William and Stephanie Sick Dean of Engineering at Rice University; head of the Statistical Sciences group at Los Alamos National Laboratory; professor of statistics at Kansas State University; and Statistics Program director at the National Science Foundation.
Keller is an elected member of the U.S. National Academy of Engineering. She has served as a member of the National Academy of Sciences Board on Mathematical Sciences and Their Applications and the Committee on National Statistics, and as chair of the Committee on Applied and Theoretical Statistics. She is a fellow of the American Association for the Advancement of Science, an elected member of the International Statistics Institute, and a fellow and past president of the American Statistical Association. Keller earned her B.S. and M.S. in mathematics from the University of South Florida and her Ph.D. in statistics from Iowa State University.
The Role of Statistics for Public Good and Good Governance
Description: Statistics provides the essential framework for developing and evaluating evidence-informed public policy and governance operations. This panel will focus on emerging pressures and opportunities on statistics related to the public good as well as ways in which young statisticians can become involved in this area through research and careers.
About the Panellists
About Josée Bégin: Josée Bégin has a master’s degree in mathematics and statistics (MSc) from the University of Ottawa. She started her career at the Canada Revenue Agency in 1994 before joining Statistics Canada in 2002, where she gained experience in overseeing large and complex statistical programs. Josée became the Assistant Chief Statistician of the Social, Health and Labour Statistics Field in January 2023.
The Social, Health and Labour Statistics Field provides accurate, timely and relevant information across a range of social topics to decision makers at all levels of government, non-governmental organizations, researchers and the Canadian public. Its portfolio includes a number of large survey and administrative data programs, such as the Centre for Population Health Data; the Canadian Centre for Justice and Community Safety Statistics; the Centre for Gender, Diversity and Inclusion Statistics; and the Centre for Labour Market Information. This field is also home to Canadian census content expertise.
Her favourite hobbies include yoga and reading.
About F. Jay Breidt: F. Jay Breidt, PhD, is a Senior Fellow in the Department of Statistics and Data Science at NORC at the University of Chicago. He is also Professor Emeritus and past Chair of the Department of Statistics at Colorado State University. His expertise is mathematical statistics, with interests that include survey sampling, time series, nonparametric regression, and uncertainty quantification for complex scientific models. Breidt has an extensive record of refereed publications and has presented over 130 invited short courses, conference talks, and academic seminars. Breidt has been an associate editor for seven different journals and Reviews Editor for the Journal of the American Statistical Association. He has served on six review committees for the National Academy of Sciences and has served two terms on the Federal Economic Statistics Advisory Committee. He currently chairs the Census Scientific Advisory Committee for the US Census Bureau. Breidt is an elected Fellow in both the American Statistical Association and the Institute of Mathematical Statistics.
About Dave Campbell: Dr. Dave Campbell is a Full Professor in the School of Mathematics and Statistics and the School of Computer Science at Carleton University. Academically, he runs a collaborative team researching inferential algorithms at the intersections of statistics with machine learning, computing, and applied mathematics to solve problems inspired by industry and government collaborations. He has co-authored discussion papers in Bayesian Analysis and the Journal of the Royal Statistical Society (Series B) and been awarded over $3.5 million in research grants.
Dave’s career path maintains a theme of Data Science leadership. He spent two years leading a Data Science team at the Bank of Canada in projects relating to cybersecurity, forecasting banknote demand, understanding drivers of inflation, ensuring data privacy, and more. Before moving to Ottawa in 2019, Dave was a Professor at Simon Fraser University, where he led the creation of their BSc in Data Science. He was the inaugural President of the Data Science and Analytics Section of the Statistical Society of Canada and was a co-organizer of the Vancouver Learn Data Science Meetup.
Find him on LinkedIn: https://www.linkedin.com/in/drdavecampbell/
About Sallie Ann Keller: See the Keynote Lecture section above.