Scientific Advisory Committee

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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

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)

Don Estep

Donald Estep is the Scientific Director of CANSSI. He recently joined the Department of Statistics and Actuarial Science as Canadian Research Chair in Computational Probability and Uncertainty Quantification 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.

Marina Vannucci (term ends 2021)

Marina Vannucci

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

Naisyin Wang is a professor in the Department of Statistics at the University of Michigan. Before joining the University of Michigan, she was a faculty member at Texas A&M University for 18 years. Her research interests include model-based clustering, mixed-effects models, measurement errors and missing data problems, non- and semiparametric modeling, and applications in biology and medicine.

Naisyin received her Ph.D. degree from Cornell University. She is a Fellow of the American Association for the Advancement of Science (AAAS), the American Statistical Association (ASA), the Institute of Mathematical Statistics (IMS), and an elected member of the International Statistical Institute (ISI). She served on the International Biometric Society (IBS) Eastern North American Region (ENAR) Regional Committee (RECOM), the Council of the IMS, and was a former president of the International Chinese Statistical Association (ICSA). She served as a co-editor of Biometrics and Statistica Sinica.

Corina Constantinescu (term ends 2023)

Corina Constantinescu

Corina Constantinescu is Professor of Mathematics and Director of the Institute for Financial and Actuarial Mathematics, in the Department of Mathematical Sciences, at the University of Liverpool. Prior to being an academic, Corina worked as an actuary and led the life insurance department of one of the first private Romanian insurance companies. Given her practical perspective, many of her papers are published in actuarial journals, however she also publishes in applied probability journals. 

She serves as associate editor in a number of actuarial journals and is part of the publicity team of Bernoulli Society for Mathematical Statistics and Probability.  Since 2013, together with Professor Severine Arnold of HEC Lausanne, they are organizing the PARTY conferences for young researchers ( Since 2018, she is regularly teaching and supervising MSc students from the African Institute of Mathematical Science (AIMS) network. 

Her expertise is in analytical methods for deriving exact or asymptotic results for ruin probabilities, with light or heavy-tailed assumptions in complex insurance risk models. Some of her more recent research interests are around fair insurance pricing when gender is not considered a factor, as well as financial inclusion, specifically fair pricing and regulation of microfinance and microinsurance practices.

Josée Dupuis (term ends 2023)

Josée Dupuis

Josée Dupuis, Ph.D., is a Professor and Chair of Biostatistics at Boston University School of Public Health. She previously held a faculty position at Northwestern University and a senior statistical geneticist position at Genome Therapeutics Corporation, a small biotech company. She has extensive experience in the development and application of methods for genome-wide association studies, gene by environment interaction investigation, genetic meta-analysis, rare variant analysis, and omics data analysis, with special emphasis on the development of novel statistical approaches to analyze genetic data collected on large families. She is involved in the Framingham Heart Study and multiple international consortia, collaborating on projects to identify genes influencing diabetes related traits, pulmonary function traits, and Alzheimer’s Disease.  She is a Fellow of the American Statistical Association (ASA) and the American Association for the Advancement of Science (AAAS), and she is past-President of the International Genetic Epidemiology Society.  She was recently honored with the International Genetic Epidemiology Leadership Award for her substantial contributions to the field and her service to the Society, and she received the 2020 American Society of Human Genetics Mentorship Award.  She is an Associate Editor for the journals Biostatistics and the Annals of Applied Statistics.

Sujit Ghosh

Sujit Ghosh (term ends 2023)

Professor Sujit Kumar Ghosh is currently a Professor in the Department of Statistics at North Carolina State University (NCSU) in Raleigh, NC, USA. He has over 25 years of experience in conducting, applying, evaluating and documenting statistical analysis of biomedical and environmental data. He has served as the Co-Director of Graduate Programs in Statistics at NCSU managing over 150 students annually during 2010-2013, the Project Director of a training program for undergraduates funded by the NSF during 2007-2013. He has served as the Program Director in the Division of Mathematical Sciences (DMS) within the Directorate of Mathematical and Physical Sciences (MPS) at NSF in 2013-2014. He has also served as the Deputy Director at the Statistical and Applied Mathematical Sciences Institute (SAMSI) in 2014-2017.

Prof. Ghosh is actively involved in teaching, supervising and mentoring graduate students. He has supervised over 35 doctoral graduate students and 5 post-doctoral fellows. He was awarded Cavell Brownie Mentoring Award at NCSU Statistics in 2014. He has also served as a statistical investigator and consultant for over 40 different research projects funded by various leading private industries and federal agencies (e.g., BAYER, CDC, GSK, MERCK, NIH, NISS, NSF, SAS, U.S.EPA, USDA-NASS etc.). Prof. Ghosh has published over 120 refereed journal articles in the various areas area of statistics with applications in biomedical and environmental sciences, econometrics and engineering. Prof. Ghosh has been regularly invited by peer institutions and conference organizers to present talks. In recent years, Prof. Ghosh has contributed significantly in developing statistical models and associated methodologies for various inferential problems that are subject to shape constraint. In 2016, Prof. Ghosh was invited to present the prestigious Helen Barton Lecture Series in Mathematical Sciences at the University of North Carolina at Greensboro where he delivered a series of three lectures on Statistical Inference Subject to Shape Constraint. Prof. Ghosh received the International Indian Statistical Association (IISA) Young Investigator Award in 2008; was elected a Fellow of the American Statistical Association (ASA) in 2009 and was elected as the President of the NC Chapter of ASA in 2013 and also the President of the IISA in 2017. He also received the Honorary Doctorate in Statistics at Thammasat University (Thailand) in 2015.

Bin Yu

Bin Yu (term ends 2023)

Bin Yu is Chancellor’s Distinguished Professor and Class of 1936 Second Chair in the departments of statistics and EECS at UC Berkeley. She leads the Yu Group which consists of 15-20 students and postdocs from Statistics and EECS. She was formally trained as a statistician, but her research extends beyond the realm of statistics. Together with her group, her work has leveraged new computational developments to solve important scientific problems by combining novel statistical machine learning approaches with the domain expertise of her many collaborators in neuroscience, genomics and precision medicine. She and her team develop relevant theory to understand random forests and deep learning for insight into and guidance for practice. 

She is a member of the U.S. National Academy of Sciences and of the American Academy of Arts and Sciences. She is Past President of the Institute of Mathematical Statistics (IMS), Guggenheim Fellow, Tukey Memorial Lecturer of the Bernoulli Society, Rietz Lecturer of IMS, and a COPSS E. L. Scott prize winner.She is serving on the editorial board of Proceedings of National Academy of Sciences (PNAS) and the scientific advisory committee of the UK Turing Institute for Data Science and AI.

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