Mark Schmidt aims to understand and speed up the algorithms used in machine learning.
Big data and big models
Mark Schmidt explores the challenges that come with learning complicated models from large datasets. His work is mainly focused on accelerating and verifying fundamental machine learning algorithms. Mark works in the areas of optimization for machine learning, probabilistic machine learning, computer vision applications among others. He has published papers on gradient methods, on improving the speed of convergence and on combining optimization methods. Through his work, Mark improves the speed, efficiency and effectiveness of machine learning models. He has applied his work in computer vision toward recognizing distinct objects in images, outdoor image segmentation and depth estimation and for image restoration and inpainting. He has also developed applications to analyse the propagation of ideas in social networks, for natural language sequence labeling and for modeling the kinematics of DNA strands.
Mark is a professor in the Department of Computer Science at the University of British Columbia. His research focuses on developing faster algorithms for large-scale machine learning, and exploring applications of machine learning. He is a Canada Research Chair, Alfred P. Sloan Fellow, NSERC Arthur B. McDonald Fellow, CIFAR Canada AI Chair with the Alberta Machine Intelligence Institute (Amii), and was awarded the 2018 SIAM/MOS Lagrange Prize in Continuous Optimization with Nicolas Le Roux and Francis Bach.
Previously, Mark was a CIFAR Senior Fellow in the Learning in Machines and Brains program and an Alfred P. Sloan Research Fellow. He has co-authored 85 papers, which have appeared in venues such as the International Conference on Machine Learning (ICML), the Neural Information Processing Systems (NeurIPS) conference, and the International Conference on Artificial Intelligence and Statistics (AISTATS). Since beginning his appointment at the University of British Columbia in 2014, Mark has supervised and co-supervised 20 early-career researchers at the M.Sc. and Ph.D. levels. He has been a Senior Program Committee member or Area Chair for several international conferences, including NeurIPS, ICML, ICLR and IJCAI.
Mark was awarded the Dorothy Killam Fellowship for his significant impact in the fields of numerical optimization and machine learning in 2025.
