The Scientific Advisory Committee adjudicates competitions for Collaborative Research Team projects and major workshops and conferences, and makes funding recommendations to the Board. The committee is chaired by the Director of CANSSI, and consists of nine prominent statistical scientists, normally from outside Canada. Each of PIMS, Fields and CRM are entitled to nominate a member of this committee.
Amy Braverman (term ends 2021)
Amy Braverman is a Principal Statistician at the Jet Propulsion Laboratory in Pasadena, California. She received her doctorate in statistics from the University of California, Los Angeles (UCLA), a masters in Mathematics from UCLA, and a B.A. degree in economics from Swarthmore College, Swarthmore, PA, in 1982.
Her research interests include information-theoretic approaches for the analysis of massive data sets, data fusion methods for combining heterogeneous, spatial and spatio-temporal data, and statistical methods for the evaluation and diagnosis of climate models, particularly by comparison to observational data. Amy focuses on the use of remote sensing data, and has designed and analyzed new Level 3 data products for MISR and other NASA missions.
Daniela Calvetti (term ends 2022)
Daniela Calvetti is the James Wood Williamson Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western University, past Simons Foundation Fellow, winner of the Mather Spotlight Prize for Women’s Scholarship, and plenary speaker at the SIAM Conference on Uncertainty Quantification. Her research interests include numerical analysis, scientific computing, computational and statistical inverse problems, and medical applications.
Merlise Clyde (term ends 2022)
Merlise Clyde is a professor in the Department of Statistical Science at Duke University. She is Fellow of the ASA, past President of the International Society of Bayesian Analysis, and winner of the International Society of Bayesian Statistics Zellner Medal. Her research interests include Bayesian solutions to the related problems of feature/variable selection, model selection and prediction using an ensemble of models to account for model uncertainty using Bayesian Model Averaging, with an emphasis on prior choice and computation.
Donald Estep (Chair of the Scientific Advisory Committee)
Donald Estep is the Scientific Director of CANSSI. He recently joined the Department of Statistics and Actuarial Science at Simon Fraser University, moving 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 a 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.
Yulia R. Gel is Professor in the Department of Mathematical Science at the University of Texas at Dallas. Her research interests include statistical foundation of data science, inference for random graphs and complex networks, time series analysis, and predictive analytics. She received her Ph.D in Mathematics from Saint Petersburg State University, Russia, followed by a postdoctoral position in Statistics at the University of Washington. Prior to joining UT Dallas, she was a faculty member at the University of Waterloo, Canada. She also held visiting positions at Johns Hopkins University, University of California, Berkeley, and the Isaac Newton Institute for Mathematical Sciences, Cambridge University, UK. Yulia is a Fellow of the American Statistical Association and is a recipient of the Abdel El-Shaarawi Young Researcher’s TIES Award for outstanding contributions to the field of environmental statistics.
Xuming He is H. C. Carver Collegiate Professor and Chair of Statistics at the University of Michigan. His prior appointments include faculty positions at National University of Singapore and University of Illinois at Urbana-Champaign, and Program Director of Statistics at the US National Science Foundation. His research interests include theory and methods in robust statistics, quantile regression, re-sampling, and model selection. His interdisciplinary research aims to promote the better use of statistics in biosciences, climate studies, kinesiology, and social-economic studies.
Xuming He is a Fellow of the American Association for the Advancement of Science (AAAS). He served on the Council of the Institute of Mathematical Statistics (IMS) and the International Statistical Institute (ISI) and was a former president of the International Chinese Statistical Association (ICSA). He served as co-editor of Journal of the American Statistical Association, and also served as Program Chair of the 2010 Joint Statistical Meetings and the 2013 World Statistics Congress.
Douglas Nychka (term ends 2021)
Douglas Nychka is a statistical scientist whose areas of research include the theory, computation and application of curve and surface fitting with a focus on geophysical and environmental applications. His current interests are in quantifying the uncertainty of numerical experiments that simulate the Earth’s present and possible future climate. His statistical expertise is in spline and spatial statistical methods especially as they are applied to large geophysical data sets and numerical models.
He has a Ph. D. in Statistics (1983) from the University of Wisconsin and he subsequently spent 14 years as a faculty member at North Carolina State University. He assumed leadership of the Geophysical Statistics Project (GSP) at the National Center for Atmospheric Research in 1997. GSP is a program funded by the National Science Foundation to develop collaborative research and training between statistics and the geosciences. In 2004, he became Director of the Institute of Mathematics Applied to Geosciences.
He has received the Jerry Sacks Award for Multidisciplinary Research (2004), the Distinguished Achievement Award Section on Statistics in the Environment (2013), and the Achievement Award for the International Statistics and Climatology Meeting (2013). He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics.
David Stephens is James McGill Professor and Chair of the Department of Mathematics and Statistics at McGill University, Montreal. His research interests include Bayesian inference and computation, and applications in biostatistics, causal inference, statistical genetics and bioinformatics. He was Editor in Chief of the Canadian Journal of Statistics, 2013-2015, and is a CRM StatLab member.
Marina Vannucci (term ends 2021)
Marina Vannucci received a Laurea (B.S.) in Mathematics in 1992 and a Ph.D. in Statistics in 1996, both from the University of Florence, Italy. Prior to joining Rice University in 2007, she was Research Fellow at the University of Kent at Canterbury, UK, during 1996-1998. In 1998 she joined the Department of Statistics at Texas A&M University, as Assistant Professor, became Associate Professor in 2003 and Full Professor in 2005. She is currently Noah Harding Professor of Statistics and Department Chair at Rice University and holds an Adjunct appointment with the Department of Biostatistics at the UT MD Anderson Cancer Center.
Marina is generally interested in the development of statistical models for complex problems. Her methodological research has focused in particular on the theory and practice of Bayesian variable selection techniques and on the development of wavelet-based statistical models and graphical models. She has developed methodologies that have found applications in chemometrics, high-throughput genomics and neuroimaging. Marina has published over 130 research papers, co-edited 3 books and delivered more than 170 invited presentations. She has supervised 21 Ph.D. students and 8 postdoctoral fellows, since 1998.
Marina was the recipient of an NSF CAREER award in 2001 and won the Mitchell prize from the International Society for Bayesian Analysis in 2003. She is an elected Member of the International Statistical Institute (ISI), since 2007, and an elected Fellow of the American Statistical Association (ASA), since 2006, the Institute of Mathematical Statistics (IMS), since 2009, the American Association for the Advancement of Science (AAAS), since 2012, and the International Society for Bayesian Analysis (ISBA), since 2014. She was the 2018 President of ISBA, she has served on the editorial boards of several journals and was the Editor-in-Chief of the journal Bayesian Analysis, the flagship journal of ISBA, in 2013-2015.
Naisyin Wang (term ends 2022)
Naisyin Wang is a professor in the Department of Statistics at University of Michigan, Fellow of the AAAS, Fellow of the ASA, Fellow of the IMS, and Elected member of the ISI. Her research interests include model based clustering, mixed effects models, non- and semiparametric modeling, and applications in biology and medicine.