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Mark Schmidt is a Professor in the Department of Computer Science at the University of British Columbia. He has made significant contributions to the fields of algorithms, mathematical programming, and machine learning, particularly large-scale machine learning. His research interests encompass optimization methods, probabilistic machine learning, and decision-making processes in complex environments. Schmidt is recognized with prestigious awards such as the Alfred P. Sloan Fellowship and the Canada CIFAR AI Chair, signifying his impactful work in artificial intelligence. He has a wealth of experience in academia with multiple postdoctoral positions and has held faculty roles at UBC, progressing through ranks from Assistant Professor to Professor. His teaching spans various machine learning courses, and he engages in mentoring students in his lab, contributing actively to research and development in machine learning techniques. Schmidt's work has been published in top-tier conferences, reflecting his deep involvement in advancing knowledge and methods in computer science, and he continues to lead initiatives that shape the future of machine learning.
University of British Columbia • Vancouver, BC, Canada
Leading research and teaching in algorithms and machine learning.
University of British Columbia • Vancouver, BC, Canada
Conducting advanced research and teaching on complex machine learning systems.
University of British Columbia • Vancouver, BC, Canada
Focused on teaching and research in machine learning.
Simon Fraser University • Burnaby, BC, Canada
Conducted research within the Natural Language Lab.
Ecole Normale Superieure • France
Contributed to the INRIA SIERRA project.
Offers course-only and thesis routes. Focus areas include philosophy of science, mind, ethics, and Asian philosophy.