Dr. Mark Schmidt

Canada Research Chair in Large-Scale Machine Learning​

Mark Schmidt is an associate professor in the Department of Computer Science at the University of British Columbia and has been with the department since 2014. His 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 finished his M.Sc. in 2005 at the University of Alberta working as part of the Brain Tumor Analysis Project, and his Ph.D. in 2010 at the University of British Columbia working 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.

Research Interests

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