2021 CANSSI Showcase

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Friday, November 19 – Saturday, November 20, 2021

Overview

CANSSI will be hosting a virtual event celebrating the work of our researchers, postdocs, and students. This two-day event, running Friday, November 19 and Saturday, November 20, will be a great opportunity for you to meet the national statistical and data science community, hear about career opportunities, and learn about different ways CANSSI can support your work. This event includes:

  • A community town hall
  • Presentations by CANSSI Collaborative Research Teams (CRT)
  • A panel session
  • Lightning talks
  • Poster session for postdocs, grads and undergrad students
  • A social/networking event

Poster Session

Participating in the poster session allows other academics and researchers to see your work. You’ll get a chance to network with faculty across the country during the poster session. Limited spaces available.

Register to present at the Poster Session.

Lightning Talks

There will be lightning talks, which are short, 10 minute presentations. These talks offer a great opportunity to share your work with other academics and researchers, and to hone your presentation skills.

EDI Town Hall

The Statistical Society of Canada and CANSSI are organizing a townhall on their Equity, Diversity and Inclusion activities, policies, and programs. After short overviews of the programs, we will organize a community discussion. We are seeking evaluations, experiences, and new ideas from the community. The goal is to help ensure that our EDI activities and programs are community driven.

Schedule

Day 1: Friday, November 19

TIME
(EST / PST)
EVENT
12:00 / 9:00Welcome and Elder Opening [Room 1]
12:15 / 9:15CRT Presentations by Mahmoud Torabi and Changbao Wu [Room 1]
13:45 / 10:45Break
14:00 / 11:00CANSSI Program Overview – Meet the Directors [Room 2]
14:45 / 11:45Break
15:00 / 12:00Lightning Talks [Room 3]
16:30 / 13:30Break
16:45 / 13:45EDI Townhall [Room 3]

Day 2: Saturday, November 20

TIME
(EST / PST)
EVENT
12:00 / 9:00Welcome and Elder Opening [Room 4]
12:15 / 9:15Workshop: Innovations in Data Analytics and
Data Science: What’s New at Statistics Canada?
[Room 4]
14:45 / 11:45Break
15:00 / 12:00Lightning Talks [Room 5]
16:00 / 13:00CRT Presentations by Tianyu Guan and Ken Peng [Room 6]
16:45 / 13:45Closing Remarks and Elder Closing [Room 6]
17:00 / 14:00Poster Session, Networking & Social Hour

CRT Presentation Summaries

Sports Analytics Using Event Data and Tracking Data 
Presented by Tianyu Guan

Summary
This presentation will first talk about a project in the National Rugby League (NRL) which aims to develop new methods for providing instantaneous in-game win probabilities. Besides the score differential, betting odds and real-time features extracted from the match event data are also used as inputs to inform the in-game win probabilities. Rugby matches evolve continuously in time and the circumstances change over the duration of the match. Therefore, the match data are considered as functional data and we propose methods to estimate the in-game win probabilities from the perspective of functional data analysis. I will then introduce our recent research on the strategy of playing defensively in soccer. The analysis is predicated on the assumption that the area of the convex hull formed by the players on a team provides a proxy for defensive style. It is assumed that smaller areas coincide with a greater defensive focus. With the availability of tracking data, the area of the convex hull can be obtained. Finally, I will outline the goals of our CRT.

About Tianyu Guan
Tianyu joined the Department of Mathematics and Statistics at Brock University as an Assistant Professor in 2020. She completed her BSc in Statistics from Jilin University in 2011. She received her MSc in Actuarial Science from Simon Fraser University in 2014 and then obtained her PhD in Statistics in 2020. Her research interests are sports analytics, functional data analysis and data science.

Applying Bayesian Methods to the Impulse-Response Modelling of Elite Middle-Distance Runner Performance
Presented by Ken Peng

Summary
The impulse-response (IR) model describes the relationship between athlete training history and performance. The model features five parameters, with two derived parameters providing context for interpretation in terms of exercise training. Despite some past successes, IR models are often poorly fitted. Here we describe a novel Bayesian approach to fit the IR model. We discuss the elicitation of informative priors, and justify the assumption that performance is multivariate normal distributed. MCMC via Gibbs sampling was used to sample the posterior. The method was applied to an international-class middle-distance runner, for which training was quantified as TRIMPi and performance as IAAF points achieved in a sanctioned race. The method produced well-constrained estimates of the five parameters, but the posterior intervals of the derived parameters were too wide to make reliable training optimization decisions. We conclude our approach could further improve the fit of the IR model.

About Ken Peng
Ken is a 2nd-year master’s student majoring in Statistics. Ken is a big fan of sports analytics and he finished third in the Young Investigator Award competition at the Sport Innovation Summit 2020. His master’s thesis includes the Bayesian implementation of the critical velocity model which is well-known in sports science, and he is going to be defending in the Fall.

A Review on Some Recent Advances in Infectious Disease Modeling
Presented by Mahmoud Torabi

Identifying disease clusters and spatial patterns of disease (from human/animal/plant) is important to inform policy makers, programs and interventions at both local and global scales. The recent emergence of the coronavirus as a global pandemic is one example of a critical public health threat that challenged management systems. The rapid spread of coronavirus across much of the globe is not well understood yet. Space-time patterns of spread span multiple scales due to complex disease ecological processes and biases from surveillance data generated from multi-jurisdictions with varying sampling protocols are real challenges. Our CRT members have spearheaded innovation in disease modeling by addressing several practical problems related to infectious diseases in environment and health by advancing statistical modeling techniques. In this talk, I will review some projects done by our CRT members in the context of infectious disease modeling. 

About Mahmoud Torabi
Mahmoud is a Professor of Biostatistics in the Department of Community Health Sciences at the University of Manitoba. He is also an Adjunct Professor in the Department of Statistics and a scientist in the Children’s Hospital Research Institute of Manitoba (CHRIM) at the University of Manitoba. His main research areas are spatial statistics and small area estimation. He has received some provincial (Research Manitoba, CHRIM), and national funding (NSERC DG, NSERC Alliance, NSERC EIDM, CANSSI-CRT, CIHR) for his research as principal investigator (PI) and co-PI. He has published more than 65 papers in peer-reviewed statistics and health research journals, and served as a referee for over 100 papers. He has served the Statistical Society of Canada in various capacities including President of the Survey Methods Section. 

Empirical Likelihood Inference for Non-probability Survey Samples Presented by Changbao Wu

Summary
In this presentation, we first provide an overview of two major developments on complex survey data analysis: the empirical likelihood methods and statistical inference with non-probability survey samples. We then propose new inferential procedures on analyzing non-probability survey samples through the pseudo empirical likelihood approach. The proposed methods lead to asymptotically equivalent point estimators that have been discussed in the recent literature but possess more desirable features on confidence intervals such as range-respecting and data-driven orientation. We conclude with some further discussions on issues and problems related to the topic.

About Changbao Wu
Changbao is Professor of Statistics in the Department of Statistics and Actuarial Science at University of Waterloo. His main research interests include design and analysis of complex surveys, resampling techniques, missing data analysis and causal inference, and data integration. He is Fellow of ASA, Fellow of IMS, Elected Member of ISI, and was the winner of the CRM-SSC Prize in Statistics in 2012. He has served on several editorial boards including CJS, JASA and Biometrika. He is the lead author of the book Sampling Theory and Practice (with Mary Thompson) published by Springer in 2020.

MC: Nancy Reid, Professor, Department of Theoretical Statistics, University of Toronto

Nancy Reid studied at the University of Waterloo (B.Math. 1974), the University of British Columbia (M.Sc. 1976), Stanford University (PhD 1979) and Imperial College, London (PDF 1980). She joined the University of Toronto in 1986 from the University of British Columbia. She has held several leadership roles in statistical science including Chair of the Department (1997–2002) and Scientific Director of the Canadian Statistical Sciences Institute (2015–2019). Nancy is a Fellow of the Royal Society, the Royal Society of Canada, the Royal Society of Edinburgh, and a Foreign Associate of the National Academy of Sciences. In 2015 she was appointed Officer of the Order of Canada.

Panel Session

Innovations in Data Analytics and Data Science: What’s New at Statistics Canada?

This panel session will feature developments in data analytics and data science at Statistics Canada, including simulation methods and datasets, new research projects, available data resources, and innovative processes and practices to ensure analytic approaches are transparent and equitable.

Speakers:

Isabelle Michaud, Senior Methodologist, Statistics Canada 
Isabelle Michaud is a Senior Methodologist at Statistics Canada where she has worked since 2005. She holds a Bachelor’s and a Master’s degree in Statistics from Université Laval, as well as a Master’s degree in Physical Activity Sciences fromUniversité de Montréal. Her work at Statistics Canada involves consultations on the appropriate use of data analysis tools and methods, for the employees of the Agency or other departments, as well as researchers from academia or Research Data Centres (RDCs). Isabelle also develops and presents training on topics of data analysis.

She also collaborates on analytical studies with researchers in the health field, mostly on the measurement of physical activity and sedentarity behaviour, as well as the measurement of workplace mental health. Isabelle recently joined the confidentiality group where she is developing a synthetic dataset with the team. She is mostly working on assessing the risks of disclosure while making sure the synthetic file keeps its analytical utility.

Deirdre Hennessy, Senior Research Analyst, Statistics Canada
Deirdre Hennessy is a Senior Analyst in the Health Analysis Division at Statistics Canada and the Lead Epidemiologist of the Personal Protective Equipment (PPE), Supply and Demand Model. The primary focus of her research has been the development of microsimulation models of chronic disease. Since April 2020 she has been involved in the epidemiological modelling of COVID-19.

Saeid Molladavoudi, Senior Data Science Advisor, Statistics Canada
Saeid Molladavoudi is the Senior Data Science Advisor in the Data Science Division at Statistics Canada. He provides advisory services to Statistics Canada’s employees in a wide array of data techniques and issues related to data science, as well as strategic advice to senior management in support of statistical programs. He is responsible for exercising a catalytic role in the implementation of the Data Science strategy and actively connecting and being involved in national and international data science activities and projects. In this technical role, he acts as ambassador of Data Science performing collaborative research and creating and maintaining partnerships with internal and external stakeholders. His expertise in identifying opportunities and determining effective road maps helps him bring together ideas, individuals and technologies to obtain valuable information from data at large scale.

Loïc Muhirwa, Methodologist, Statistics Canada
Loïc Muhirwa is a Methodologist and a Data Scientist working in the Data Science Division at Statistics Canada. His work involves the design, implementation and deployment of Natural Language Processing (NLP) systems, the development of a responsible machine learning framework and explainable and interpretable AI research. He is also responsible for peer-reviewing proof-of-concept machine learning projects before they transition to production. He is concurrently a graduate student at the University of Ottawa where he studies machine learning applications to computer vision problems in medical imaging.

Moderator:

Lisa Lix , Professor, Department of Community Health Sciences, University of Manitoba and Tier 1 Canada Research Chair in Methods for Electronic Health Data Quality 
Lisa Lix is a Professor in the Department of Community Health Sciences at the University of Manitoba and a Tier 1 Canada Research Chair in Methods for Electronic Health Data Quality. Her research expertise lies in statistical methods for complex healthcare data and patient-reported outcome measures. She collaborates widely with research groups and organizations across Canada, including Health Data Research Network Canada, Canadian Network for Observational Drug Effect Studies, and the Public Health Agency of Canada.

EDI Townhall Speakers

Bouchra Nasri is Assistant Professor at the School of Public Health of Univesité de Montréal. Her research interests are dependence modelling, time series, and more recently spatial modelling. She also authored and co-authored several R packages.

The main applications targeted by her research projects are related to climate change, public health, and infectious diseases modelling. Bouchra is an associate director of the new infectious diseases network OMNI-RÉUNIS and co-lead of the data theme for the network.  Her work is funded by multiple provincial and federal agencies. Her work is statistics is found mainly by NSERC and FRQNT. Her applied work in public health is found by multiple source such as CIHR, CReSP, ESPUM, PHAC and more recently MfPH and OMNI-RÉUNIS. Finally, Bouchra is Chercheure boursière Junior 1 in Artificial intelligence in health and digital health

Don Estep, Scientific Director at CANSSI and Simon Fraser University Science Professor, is a mathematician and statistician who collaborates with scientists and engineers to develop algorithms and models for a range of problems in ecology, physics and engineering. He has applied his research to detect black holes, forecast hurricane storm surges, measure electromagnetic scattering and predict fusion reaction. By examining the behavior of complex multi-physics systems and subsequently creating computational and statistical models Estep can predict behavior, quantify uncertainty and ultimately mitigate hazards.

Lightning Talk Speakers

Rim Cherif, Faculty, Management, American University of Cairo 

Rim Cherif is an Assistant professor of finance with a PhD in financial Engineering from HEC Montréal.

Yi Lian, PhD, Biostatistics, McGill University 

Yi Lian is a PhD candidate in Biostatistics in the Department of Epidemiology, Biostatistics and Occupational Health, School of Population & Global Health at McGill University. He received his BSc and MSc from McGill University as well. Yi’s research interests include high dimensional statistics, structured regularization methods, optimization, etc.

Somayeh Momenyan, Postdoctoral Fellow, Department of Community Health Sciences, University of Manitoba 

Somayeh Momenyan a postdoctoral fellow at the University of Manitoba in the Department of Community Health Sciences.

Payman Nickchi, PhD, Statistics and Actuarial Sciences, Simon Fraser University

Payman Nickchi is currently a PhD candidate working under the supervision of Professor Jinko Graham at the department of Statistics and Actuarial Sciences, Simon Fraser University. His area of research is in statistical genetics and genetic epidemiology with a focus on detecting rare causal variants that contribute to diseases in the human population.

Matthew Parker, PhD, Statistics and Actuarial Sciences, Simon Fraser University 

Matthew Parker is a PhD student of statistics at Simon Fraser University, who studies population abundance modelling techniques and disease analytics.

Ridwan Sanusi, Postdoctoral Fellow, Community Health Sciences, University of Manitoba 

Ridwan Sanusi is a postdoctoral fellow at the Centre for Healthcare Innovation, University of Manitoba. He received his PhD from the Department of Systems Engineering and Engineering Management, City University of Hong Kong, Hong Kong. His PhD thesis focused on advanced statistical process monitoring schemes for industrial data. During his PhD program, he was a visiting scholar at the Department of Industrial Engineering, Tsinghua University, Beijing, China. Prior to these, he received his MSc in Applied Statistics from King Fahd University of Petroleum & Minerals, Saudi Arabia, and BSc in Statistics (First Class Honours) from University of Ibadan, Nigeria. His research focuses on Statistics, Statistical Process Monitoring, Machine Learning, and Biostatistics.

Archer Zhang, PhD, Statistics, University of British Columbia

Archer Zhang is a PhD student at the Department of Statistics, University of British Columbia. He is working on the empirical likelihood and the semiparametric density ratio model under the supervision of Jiahua Chen.

Boyi Hu, PhD, Statistics and Actuarial Science, Simon Fraser University 

Boyi Hu is a 4th-year PhD student in Statistics at Simon Fraser University, and an aspiring researcher in Statistics. His research focuses on functional data analysis. Boyi’s past work has been applied in the lumber data and climate data. During his spare time, he loves to play basketball and DOTA.

Xiaomeng Ju, PhD, Statistics, University of British Columbia 

Xiaomeng Ju is a PhD student in Statistics at the University of British Columbia, advised by Matias Salibian-Barrera. She received her BSc in Statistics from Renmin University of China, and MA in Statistics from University of Michigan. Xiaomeng’s research is centred on computational statistics with a special focus on robust statistics and functional data. Her ongoing thesis work develops gradient boosting methods for regression problems with complex data.

Haixu Alex Wang, PhD, Department of Statistics and Actuarial Science, Simon Fraser University 

Haixu Alex Wang is finishing his PhD at Simon Fraser University in the Department of Statistics and Actuarial Science. He is working under the supervision of Jiguo Cao. His research focuses are at the juncture of functional data analysis, network analysis, and data science. Haixu also has a particular research interest in computational neuroscience.

Alouette Zhang, PhD, Human Genetics, McGill University 

During Alouette’s bachelor study at McGill in Quantitative Biology, she studied the statistics from Head and Neck Squamous Cell Carcinoma with Dual Energy CT scans under Dr. Sahir Bhatnagar’s supervision. Alouette is currently a Human Genetics PhD student in Dr. Simon Gravel’s lab under the Kyoto-McGill International Collaborative Program in Genomic Medicine. She is studying how evolution affected patterns of variation encoded in our genome and among populations.

Xinyi (Cindy) Zhang, PhD, Department of Statistical Sciences, University of Toronto

Xinyi (Cindy) Zhang is a fourth year PhD candidate in the Department of Statistical Sciences at the University of Toronto. She is advised by Dehan Kong, Stanislav Volgushev and Linbo Wang. Prior to her PhD study, she received her bachelor’s degree in Mathematical Application in Economics and Finance from U of T, and her master’s degree in Statistics from the University of California at Berkeley. Her research interest lies in developing statistical methods to solve problems arising from big data and high-dimensional data, currently with a main focus on robust high-dimensional causal inference.

MC: John Braun, Professor, Department of Mathematics, Statistics, University of British Columbia 

Since completing his PhD in Statistics at the University of Western Ontario, John has held positions at a number of universities, including Western for 14 years where he attained the rank of Full Professor and was Chair of the Statistics Graduate Program for 5 years. In 2014, John took the opportunity to become Head of Computer Science, Mathematics, Physics and Statistics at UBC’s Okanagan campus. The following year he became Deputy Director of the Canadian Statistical Sciences Institute (CANSSI). John’s research in statistics has often been motivated by scientific problems, coming from psychology, biology, medicine, engineering and physics. His methodological research is concerned with smoothing and inference techniques as they apply to data visualization and process monitoring.

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