Meet Our People

Deputy Director
McGill University
andrea.benedetti@mcgill.ca

Andrea Benedetti is an Associate Professor, jointly appointed in the departments of Medicine and Epidemiology, Biostatistics & Occupational Health at McGill University.

She is primarily interested in the statistical challenges related to individual patient data meta-analysis. Andrea is a CIHR-funded biostatistician, and an author on more than 150 peer-reviewed publications.

She co-directs the DEPRESSD Project. This group has collected data from across the globe and used it to provide evidence-based information on the diagnostic accuracy of commonly-used depression screening tools. Andrea is supported by a Chercheur Boursier award from the FRQS.

Associate Director Representing Smaller Institutions
Trent University
wesleyburr@trentu.ca

Wesley Burr is is an Associate Professor of Statistics (and Chair of Department) in the Department of Mathematics at Trent University in Peterborough, Ontario.

Wesley received a BScEng in Mathematics and Engineering in 2005 from Queen’s University in Kingston, Ontario, and a PhD in Statistics in 2012 from the same institution, working under David J. Thomson. He was a postdoctoral fellow at Queen’s University from January to May 2013 and then held a Visiting Fellowship at Health Canada from June 2013 to March 2016.

His research focuses on problems at the intersection of time series analysis, spectrum estimation, and modelling, with current research focused on problems coming from environmental epidemiology. He is grateful for funding from both NSERC’s Discovery Grant program and federal agencies Health Canada, Agriculture and Agrifood Canada, and Natural Resources Canada.

Director
Simon Fraser University
donald_estep@sfu.ca

Donald Estep is the Director of CANSSI. He joined the Department of Statistics and Actuarial Science as Canada Research Chair in Computational Probability and Uncertainty Quantification at Simon Fraser University. He moved from the Department of Statistics at Colorado State University, where he was Department Chair, University Distinguished Professor and University Interdisciplinary Research Scholar.

His research interests include uncertainty quantification for complex physics models, stochastic inverse problems, adaptive computation, and modeling of multiscale systems. Working with his collaborators, he has developed a systematic approach to a posteriori error estimation for simulations of complex systems, efficient numerical methods for uncertainty quantification for physical models, and theory and solution of inverse problems for stochastic parameters in physical models.

His application interests include ecology, materials science, detection of black holes, modeling of fusion reaction, analysis of nuclear fuels, hurricane wave forecasting, flow in porous media, and electromagnetic scattering. His research has been supported by multiple government agencies and national laboratories.

Don has served on several scientific advisory panels for the U.S. National Science Foundation and Department of Energy and on the Sandia National Laboratories CISE External Review Board and has co-authored several reports. He has served as the (founding) Chair of the SIAM Activity Group on Uncertainty Quantification, (founding) Co-Editor in Chief of the SIAM/ASA Journal on Uncertainty Quantification, and as SIAM representative to the Governing Board of SAMSI.

His awards include Fellow of the Society for Industrial and Applied Mathematics, the Computational and Mathematical Methods in Sciences and Engineering (CMMSE) Prize, and the Chalmers Jubilee Professorship of Chalmers University of Technology.

Associate Director Representing Prairies
University of Manitoba
M_Jafari_Jozani@umanitoba.ca

Mohammad Jafari Jozani is currently an Associate Professor with the Department of Statistics and an adjunct professor of Biomedical Engineering at the University of Manitoba in Winnipeg.

His current research involves statistical learning problems with high dimensional aspects in biostatistics, engineering and sustainable energy; small area estimation as well as statistical inference with complex sampling designs using order statistics and rank information. The focal point of his research program is on developing new methodologies, models and computational tools to solve data driven problems in a variety of application domains.

He has applied his research in areas such as breast cancer studies, BMD analysis and osteoporosis, mercury contamination in fish bodies, and recently in the calibration problems to design simulators for training purposes in order to make surgeries safer.

Associate Director Representing Ontario
University of Toronto
lisa.strug@utoronto.ca

Lisa J. Strug is a Senior Scientist at the Research Institute of The Hospital for Sick Children and is an Associate Professor in the Department of Statistical Sciences and the Division of Biostatistics at the University of Toronto.

She is the Associate Director of The Centre for Applied Genomics, a federally funded Toronto-based genome centre and one of three centres contributing to a national platform providing genome sequencing and analysis services in Canada and Internationally.  Her research has focused on statistical genetics and genomics, on the foundations of statistics and on their intersection.

She is the associate editor and statistical genetics editor of npj Genomic Medicine and is the Tier 1 Canada Research Chair in Genome Data Sciences.

Associate Director Representing Quebec
Université Laval
denis.talbot@fmed.ulaval.ca

Denis Talbot is a professor in the Department of Social and Preventive Medicine at Université Laval. He participated in the creation of the graduate programs in biostatistics there. He was elected as a regional representative for Quebec at the Statistical Society of Canada (SSC) in 2021 for a two-year term and is also a member of the bilingualism committee and the membership committee of the SSC.

His research interests concern causal inference methods for the analysis of observational data. He is also doing collaborative work in various areas including psychosocial stressors at work, vaccine effectiveness, cardiovascular health, and cancer. His program of research has been supported by grants from NSERC, CIHR and by research-career awards from the Fonds de recherche du Québec – Santé.

Associate Director Representing Alberta, British Columbia, Yukon
University of British Columbia
lang@stat.ubc.ca

Lang Wu is a Professor in the Department of Statistics at the University of British Columbia in Vancouver. He holds a PhD in Statistics from the University of Washington in Seattle.

His research has focused on analysis of longitudinal data based on mixed effects models, joint modelling longitudinal and survival data, missing data and measurement errors, and order-restricted hypothesis testing. He has applied his research in HIV/AIDS studies, cancer studies, and other health-related areas.

Associate Director Representing Atlantic Canada
Memorial University of Newfoundland
yyilmaz@mun.ca

Yildiz Yilmaz is an Associate Professor and Deputy Head, Statistics, in the Department of Mathematics and Statistics at Memorial University in St. John’s, Newfoundland and Labrador. She joined the department in 2013 after completing her PhD at the University of Waterloo in 2009 and working as a postdoctoral fellow at the Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital and the Dalla Lana School of Public Health, University of Toronto, between 2009 and 2013.

Her research interests are in the areas of statistical theory and methodology, and in statistical methods in biostatistics and statistical genetics. In particular, her work focuses on survival analysis, event history analysis, multivariate modeling and analysis, causal inference, incomplete data analysis and response-selective sampling. Her work has been motivated by important problems in biomedicine and genetics.

She currently has the following research programs within the field of statistics, genetic epidemiology and statistical genetics: (1) development and application of novel methods to model time-to-event phenotypes in genome-wide prognosis studies; (2) development and application of novel genetic association methods based on joint models and directional models of multiple phenotypes; (3) evaluation of designs and statistical methods under response-dependent sampling; and (4) development of methods to model multivariate survival times.

Term: January 2026–December 2028

Jay Breidt 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 received his PhD at Colorado State University in 1991 and spent the first nine years of his career at Iowa State University as an assistant professor and tenured associate professor, before returning to Colorado State in 2000.

Breidt has an extensive record of refereed publications. He 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 and The American Statistician. He has served on six review committees for the National Academy of Sciences. He is past Chair of the American Statistical Association National Committee on Energy Statistics (an advisory panel for the Energy Information Administration, US Department of Energy), has served two terms on the Federal Economic Statistics Advisory Committee, and currently chairs the Census Scientific Advisory Committee for the US Census Bureau.

Breidt has received numerous honors, including recognition with a national prize in environmental statistics, elected membership in the International Statistical Institute, and elected fellowship in both the American Statistical Association and the Institute of Mathematical Statistics.

Term: January 2026–December 2028

Peter Craigmile is a Professor in the Department of Mathematics and Statistics at Hunter College, The City University of New York (CUNY). He also serves as an Honorary Research Fellow in the School of Mathematics and Statistics, University of Glasgow, Scotland. He was previously a Professor in the Department of Statistics at The Ohio State University.

His research interests include time series analysis, spatial statistics, space-time modeling, and longitudinal methods. He is interested in the use of spectral and wavelet methods to investigate dependency structures and to analyze periodicities and trends. One application of this is to the study of long memory processes. In collaboration with others, he has developed methods for spatial exceedances and extremes, which are critical to assessing spatially varying risk of environmental change or disease. He enjoys application-oriented research in areas such as Biology, Climatology, Environmental Sciences, Public Health, and Psychology.

Professor Craigmile is a fellow of the American Statistical Association, the Institute of Mathematical Statistics, and The Royal Statistical Society.

Term: January 2024–December 2026

Sandrine Dudoit is Associate Dean for the Faculty and Research in the College of Computing, Data Science, and Society, Professor in the Department of Statistics, and Professor in the Division of Biostatistics, School of Public Health, at the University of California (UC), Berkeley. She was Chair of the Department of Statistics at UC Berkeley from July 2019 to June 2022. Professor Dudoit’s methodological research interests regard high-dimensional statistical learning and include exploratory data analysis (EDA), visualization, loss-based estimation with cross-validation (e.g., density estimation, classification, regression, model selection), and multiple hypothesis testing. Much of her methodological work is motivated by statistical questions arising in biological research and, in particular, the design and analysis of high-throughput sequencing studies, e.g., single-cell transcriptome sequencing (RNA-Seq) for discovering novel cell types and for the study of stem cell differentiation. Her contributions include: exploratory data analysis, normalization and expression quantitation, differential expression analysis, class discovery and prediction, inference of cell lineages, and the integration of biological annotation metadata (e.g., Gene Ontology (GO) annotation). She is also interested in statistical computing and, in particular, computationally reproducible research. She is a founding core developer of the Bioconductor Project, an open-source and open-development software project for the analysis of biomedical and genomic data.

Professor Dudoit is a co-author of the book Multiple Testing Procedures with Applications to Genomics and a co-editor of the book Bioinformatics and Computational Biology Solutions Using R and Bioconductor. She is Associate Editor of three journals, including The Annals of Applied Statistics and IEEE/ACM Transactions on Computational Biology and Bioinformatics. Professor Dudoit was named Fellow of the American Statistical Association (2010), Elected Member of the International Statistical Institute (2014), and Fellow of the Institute of Mathematical Statistics (2021).

Professor Dudoit obtained a Bachelor’s degree (1992) and a Master’s degree (1994) in Mathematics from Carleton University, Ottawa, Canada. She first came to UC Berkeley as a graduate student and earned a PhD degree in 1999 from the Department of Statistics. Her doctoral research, under the supervision of Professor Terence P. Speed, concerned the linkage analysis of complex human traits. From 1999 to 2000, she was a postdoctoral fellow at the Mathematical Sciences Research Institute, Berkeley. Before joining the Faculty at UC Berkeley in July 2001, she underwent two years of postdoctoral training in genomics in the laboratory of Professor Patrick O. Brown, Department of Biochemistry, Stanford University. Her work in the Brown Lab involved the development and application of statistical methods and software for the analysis of microarray gene expression data.

Committee Chair

Donald Estep is the Director of CANSSI. He joined the Department of Statistics and Actuarial Science as Canada Research Chair in Computational Probability and Uncertainty Quantification at Simon Fraser University. He moved from the Department of Statistics at Colorado State University, where he was Department Chair, University Distinguished Professor and University Interdisciplinary Research Scholar.

His research interests include uncertainty quantification for complex physics models, stochastic inverse problems, adaptive computation, and modeling of multiscale systems. Working with his collaborators, he has developed a systematic approach to a posteriori error estimation for simulations of complex systems, efficient numerical methods for uncertainty quantification for physical models, and theory and solution of inverse problems for stochastic parameters in physical models.

His application interests include ecology, materials science, detection of black holes, modeling of fusion reaction, analysis of nuclear fuels, hurricane wave forecasting, flow in porous media, and electromagnetic scattering. His research has been supported by multiple government agencies and national laboratories.

Don has served on several scientific advisory panels for the U.S. National Science Foundation and Department of Energy and on the Sandia National Laboratories CISE External Review Board and has co-authored several reports. He has served as the (founding) Chair of the SIAM Activity Group on Uncertainty Quantification, (founding) Co-Editor in Chief of the SIAM/ASA Journal on Uncertainty Quantification, and as SIAM representative to the Governing Board of SAMSI.

His awards include Fellow of the Society for Industrial and Applied Mathematics, the Computational and Mathematical Methods in Sciences and Engineering (CMMSE) Prize, and the Chalmers Jubilee Professorship of Chalmers University of Technology.

Term: January 2025–December 2027

Sara Lodi is an Associate Professor of Biostatistics at the Boston University School of Public Heath. She obtained her PhD in Medical Statistics at the London School of Hygiene and Tropical Medicine, United Kingdom, in 2009. Her research focuses on causal inference methods for observational studies using big data, methods for causal inference to improve the statistical analysis of clinical trials, and the reconciliation of results from clinical trials and observational studies. She leads cutting-edge research that focuses on the translation of methodologic advancements in causal inference into actionable findings for real-world public health research questions, mainly in the areas of infectious diseases (HIV, hepatitis C, tuberculosis) and substance use disorder. She has published many articles on behalf of large international collaborations of HIV cohorts such as CASCADE, COHERE and the HIV-CAUSAL, and HepCAUSAL collaborations.

Term: January 2024–December 2026

J. Sunil Rao has been Professor in the Division of Biostatistics at the University of Minnesota and Director of Biostatistics at the University of Minnesota Masonic Comprehensive Cancer Center since January 1, 2023. From 2010 to 2022, he was the Director of the Division of Biostatistics in the Department of Public Health Sciences at the University of Miami, Miller School of Medicine. From June 2016 to December 2019, he was the Interim Chair of the Department of Public Health Sciences.

From 1998 to 2010, he was in the Department of Epidemiology and Biostatistics at Case Western Reserve University School of Medicine, where he rose to Full Professor. For the last five of those years, he was Director of the Division of Biostatistics. From 1994 to 1998, he was on faculty in the Department of Biostatistics at the Cleveland Clinic Foundation.

He graduated from the University of Toronto in 1994 with a PhD in Biostatistics under the guidance of Rob Tibshirani. In 1991, he received an MS degree in Biostatistics from the University of Minnesota, and in 1989 he received a BSc from the University of Ottawa with a double major in Biology and Biochemistry. He is a Fellow of the American Statistical Association (2011), Elected Member of the International Statistical Institute (2016), a Fellow of the Institute of Mathematical Statistics (2024), and an Honorary Member of the Society of Statistics, Computer and Applications (2024).

Term: January 2025–December 2027

Christopher K. Wikle is Curators’ Distinguished Professor of Statistics at the University of Missouri (MU), with additional appointments in Soil, Environmental and Atmospheric Sciences and the Truman School of Public Affairs. He is also the inaugural Director of the Center for Spatio-Temporal Statistics and AI at MU.  He received a PhD co-major in Statistics and Atmospheric Science in 1996 from Iowa State University. He was a research fellow at the National Center for Atmospheric Research from 1996 to 1998, after which he joined the MU Department of Statistics.

His research interests are in spatial and spatio-temporal statistics applied to environmental, ecological, geophysical, agricultural and federal survey applications, with particular interest in dynamics. His work has been concerned with formulating computationally efficient deep hierarchical Bayesian models motivated by scientific principles, with more recent work at the interface of deep neural models in machine learning.   

Awards include Fellow of the American Association for the Advancement of Science (AAAS), Fellow of the American Statistical Association (ASA) and Institute of Mathematical Statistics (IMS), elected Fellow of the International Statistical Institute (ISI), Distinguished Alumni Award from the College of Liberal Arts and Sciences at Iowa State University, ASA Environmental (ENVR) Section Distinguished Achievement Award, co-awardee 2017 ASA Statistical Partnership Among Academe, Industry, and Government (SPAIG) Award, the MU Chancellor’s Award for Outstanding Research and Creative Activity in the Physical and Mathematical Sciences, the Outstanding Graduate Faculty Award, and Outstanding Undergraduate Research Mentor Award.  His book Statistics for Spatio-Temporal Data (co-authored with Noel Cressie) was the 2011 PROSE Award winner for excellence in the Mathematics Category by the Association of American Publishers and the 2013 DeGroot Prize winner from the International Society for Bayesian Analysis. His latest book, Spatio-Temporal Statistics with R, with Andrew Zammit-Mangion and Noel Cressie, was published in 2019 and won the 2019 Taylor and Francis award for Outstanding Reference/Monograph in the Science and Medicine category. Dr. Wikle is Associate Editor for several journals and is an inaugural member of the Statistics Board of Reviewing Editors for Science.

Term: January 2024–December 2026

Dr. Alyson Wilson is the Senior Associate Vice Chancellor for Research at North Carolina State University. She is also a professor in the Department of Statistics and Principal Investigator for the Laboratory for Analytic Sciences. She is a Fellow of the American Statistical Association and the American Association for the Advancement of Science. Her research interests include statistical reliability, Bayesian methods, and the application of statistics to problems in defense and national security.

Prior to joining NC State, Dr. Wilson was a jointly appointed research staff member at the IDA Science and Technology Policy Institute and Systems and Analyses Center (2011–2013); associate professor in the Department of Statistics at Iowa State University (2008–2011); Scientist 5 and technical lead for Department of Defense Programs in the Statistical Sciences Group at Los Alamos National Laboratory (1999–2008); and senior statistician and operations research analyst with Cowboy Programming Resources (1995–1999).

Dr. Wilson is currently serving on the National Academy of Sciences Committee on Applied and Theoretical Statistics and as Chair of the Board of Trustees for the National Institute of Statistical Sciences. She is an elected member of the International Statistical Institute and a member of the NC State Research Leadership Academy. She is the former Reviews Editor for the Journal of the American Statistical Association and the American Statistician and a founder and past-chair of the American Statistical Association’s Section on Statistics in Defense and National Security.

Term: January 2025–December 2027

Shu Yang graduated from Iowa State University in 2014 with a major in Mathematics and a co-major in Statistics, working with J.K. Kim and Z. Zhu. After graduation, she joined the Harvard TH Chan School of Public Health as a postdoc with Judith Lok. She then joined North Carolina State University as a faculty member in 2016. She was promoted to Associate Professor in 2021 and became a Goodnight Early Career Innovator and a University Faculty Scholar in the same year.

Research interests:

• Survey sampling and methodology
• Missing data analysis and imputation methods
• Causal inference from longitudinal observational data
• Semiparametric efficient estimation
• Spatial data analysis, nonstationary process and spectral methods
• Individual treatment regime learning, data integration and fusion methods

In 2024, she was a recipient of the Committee of Presidents of Statistical Societies (COPSS) Emerging Leader Award.

Term: January 2026–December 2029

Dr. Jane-Ling Wang is a distinguished statistician whose contributions have significantly advanced the fields of survival analysis, functional data analysis, longitudinal data analysis, neuroimaging, dimension reduction, and statistical machine learning. She earned a Bachelor of Science in Mathematics from the National Taiwan University and a Ph.D. in Statistics from the University of California, Berkeley, where she studied under renowned statisticians Jack Kiefer and Lucien Le Cam.

Dr. Wang began her academic career as an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Iowa before joining the University of California, Davis, where she has spent the majority of her distinguished career. She also served as an Associate Professor in the Department of Statistics at the Wharton School of the University of Pennsylvania.

Her research bridges both theoretical and applied statistics and is characterized by extensive interdisciplinary collaborations with biologists, demographers, medical doctors, neuroscientists, and sociologists. Over the course of her career, she has made influential contributions to survival analysis, functional and longitudinal data analysis, joint modeling of longitudinal and survival data, neuroimaging data analysis, and dimension reduction methods. More recently, she has pioneered the integration of deep learning and neural network methodologies into the analysis of survival and functional data.

Dr. Wang’s research group has played a leading role in the development of widely used statistical software. In functional data analysis, her team developed PACE, a MATLAB package, along with its companion R package, fdapace, both of which continue to be expanded and refined. Her research group has also developed the R package JSM for the joint modeling of survival and longitudinal data.

An award-winning educator, Dr. Wang emphasizes conceptual understanding, interpretation, and statistical thinking in her teaching. She believes the ultimate goal of education is to empower students to become independent learners and critical thinkers.

Dr. Wang’s achievements have earned her international recognition. She was elected an Academician of Academia Sinica in 2022, received the Humboldt Research Award in 2020, the International Chinese Statistical Association Distinguished Achievement Award in 2018, and the Gottfried E. Noether Senior Research Award in 2016. She was named Distinguished Professor of the University of California in 2013 and elected a Fellow of the American Association for the Advancement of Science in 2011. Her additional honors include delivering the Medallion Lecture of the Institute of Mathematical Statistics in 2007, election to the International Statistical Institute in 2001, and election as Fellow of both the American Statistical Association and the Institute of Mathematical Statistics in 1998.