{"id":13589,"date":"2024-05-30T15:20:04","date_gmt":"2024-05-30T19:20:04","guid":{"rendered":"https:\/\/www.crmath.ca\/2024\/05\/30\/2024-cap-crm-prize-awarded-to-bianca-dittrich\/"},"modified":"2025-08-25T10:22:48","modified_gmt":"2025-08-25T14:22:48","slug":"2024-crm-ssc-prize-awarded-to-alexandre-bouchard-cote","status":"publish","type":"post","link":"https:\/\/www.crmath.ca\/en\/2024\/05\/30\/2024-crm-ssc-prize-awarded-to-alexandre-bouchard-cote\/","title":{"rendered":"2024 CRM-SSC Prize awarded to Alexandre Bouchard-C\u00f4t\u00e9"},"content":{"rendered":"
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This year’s winner is Alexandre Bouchard-C\u00f4t\u00e9 from the University of British Columbia.<\/p>\n<\/div>\n

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Biography<\/strong><\/p>\n<\/div>\n<\/div>\n<\/div>\n

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Alexandre Bouchard-C\u00f4t\u00e9 was born and raised in Quebec, receiving a BSc Honours from McGill University in 2005. \u00a0He continued with graduate studies at the University of California, Berkeley, receiving his PhD in 2010, completed under the supervision of Michael Jordan and Dan Klein. \u00a0He then joined the faculty in the Statistics Department of the University of British Columbia, where he is now a Professor. He credits his work ethic as well as his love for science and communication to his supportive parents, Pauline C\u00f4t\u00e9 and Serge Bouchard.<\/p>\n

Alexandre is at the forefront of developing rigorous, practical and widely recognized methods in a range of areas in Bayesian and computational statistics. His methodological contributions are in the areas of Monte Carlo methods (SMC and MCMC), graphical models, non-parametric Bayesian statistics, randomized algorithms, and variational inference. \u00a0His work has had a substantial impact on the scalability of statistical methods, making analyses tractable for challenging posterior distributions arising from complex scientific models. \u00a0He has applied his methods to answer important questions in a variety of areas, with a large body of work in phylogenetics and cancer genomics.<\/p>\n

Alexandre\u2019s ground-breaking 2018 paper, introducing the bouncy particle sampler (Bouchard-C\u00f4t\u00e9, Vollmer and Doucet, Journal of the American Statistical Association), has already garnered over 250 citations (Google Scholar). \u00a0This paper provides practical methodologies for employing non-reversible Markov processes to approximate complex posterior distributions. The paper also set the scene for the development of a wide range of piecewise deterministic Markov processes for simulation by Alexandre and others. \u00a0Alexandre continues to be a leading researcher in the area of non-reversible Monte Carlo methods. In a series of subsequent methodology papers published in the Journal of Royal Statistical Society, Series B, the Journal of the American Statistical Association, and venues in machine learning, Alexandre and his collaborators have redefined how annealing samplers are used and understood. These novel non-reversible annealing methods have already been adopted in several disciplines as varied as political science, genomics, econometrics, astronomy, epidemiology and chemistry. In addition to methodology development and theoretical analysis, Alexandre\u2019s contributions in this area also include open-source software development, with a special attention to validation and performance.<\/p>\n

Alexandre\u2019s work on inference for phylogenetic trees has had a huge practical impact. \u00a0His work provides researchers with powerful tools to trace ancestral histories, with importance in areas such as tracking virus origins and reconstructing the history of ancient and modern languages. \u00a0Phylogenetic trees are also used to model the evolution of a patient\u2019s cancer, which, naturally, involves large data sets of genomic information. \u00a0Alexandre\u2019s methods have made possible the study of large populations of individually sequenced cancer cells, leading to many critical insights into cancer development and treatment. His collaborative work in cancer genomics has appeared in prestigious journals such as Nature, Nature Methods, Genome Biology and Cell. \u00a0One highlight of this line of work is the PyClone method, a Bayesian non-parametric model and MCMC sampler developed in collaboration with the Aparicio and Shah labs then at the BC Cancer Agency. PyClone has become the standard tool to perform tumour deconvolution, a key analysis step in many cancer genomics workflows. This work has been cited more than 900 times.<\/p>\n

Alexandre is known for the excellence of his training and mentoring of young researchers. \u00a0He has supervised 12 Ph.D. students, 13 MSc students and two postdoctoral fellows. Of these, many are award winners (including a recent Pierre Robillard Award), one is already a tenured faculty member at Simon Fraser University, another is an Assistant Professor at the University of Toronto and two work at Google. His contributions to the profession go beyond research: he has served as Graduate Program Chair in his home department, and is currently the Treasurer of the SSC Biostatistics Section. He is also Associate Editor for the Journal of the American Statistical Society and the Journal of Machine Learning Research.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>

About the CRM-SSC Prize<\/h2>
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The CRM-SSC Prize in Statistics recognizes a statistical scientist’s excellence and accomplishments in research during the first fifteen years after earning his\/her doctorate (or equivalent degree). It is awarded annually by the Centre de recherches math\u00e9matiques and the SSC.<\/p>\n<\/div>

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Source:<\/strong> Statistical Society of Canada<\/em><\/p>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":17,"featured_media":13593,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[55,82],"tags":[],"class_list":["post-13589","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news","category-prizes"],"_links":{"self":[{"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/posts\/13589","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/users\/17"}],"replies":[{"embeddable":true,"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/comments?post=13589"}],"version-history":[{"count":5,"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/posts\/13589\/revisions"}],"predecessor-version":[{"id":13604,"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/posts\/13589\/revisions\/13604"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/media\/13593"}],"wp:attachment":[{"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/media?parent=13589"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/categories?post=13589"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.crmath.ca\/en\/wp-json\/wp\/v2\/tags?post=13589"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}