Mark Schmidt’s research focuses on developing faster algorithms for large-scale machine learning, with an emphasis on methods with provable convergence rates and that can be applied to structured prediction problems. From 2011 through 2013 he worked at the École normale supérieure in Paris on inexact and stochastic convex optimization methods. He has worked as part of the Brain Tumor Analysis Project and on graphical model structure learning with L1-regularization. He has also worked at Siemens Medical Solutions on heart motion abnormality detection, with Michael Friedlander in the Scientific Computing Laboratory at the University of British Columbia on semi-stochastic optimization methods, and with Anoop Sarkar at Simon Fraser University on large-scale training of natural language models.
Associate Fellow Learning in Machines & Brains
University of British ColumbiaDepartment of Computer Science
M.Sc. University of Alberta
Ph.D. University of British Columbia
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