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Mark Schmidt is a Professor in the Department of Computer Science at the University of British Columbia. His research primarily focuses on optimization, machine learning, and their applications in various domains. Schmidt's significant contributions include work on large-scale machine learning, algorithms, and probabilistic reasoning, with numerous publications in top-tier conferences such as NeurIPS and ICML. He has received several notable fellowships, including the Dorothy Killam Fellowship and the Arthur B. McDonald Fellowship. Schmidt has an extensive background in academia, having held positions from Ph.D. student to Professor, and is actively involved in supervising graduate students and conducting tutorials on numerical optimization and machine learning topics. He serves as the Canada Research Chair for Large-Scale Machine Learning and has been recognized for his work with awards like the Alfred P. Sloan Fellowship. Schmidt is also a Canada CIFAR AI Chair and was a senior fellow with CIFAR, exploring learning algorithms.
University of British Columbia • Vancouver, BC, Canada
Teaching and conducting research in machine learning and optimization.
University of British Columbia • Vancouver, BC, Canada
Conducting research and teaching courses in computer science.
University of British Columbia • Vancouver, BC, Canada
Taught courses and supervised graduate students in algorithms and machine learning.
Simon Fraser University • Burnaby, BC, Canada
Conducted research in natural language processing.
Ecole Normale Superieure • Paris, France
Worked on the INRIA SIERRA project related to machine learning.
Offers course-only and thesis routes. Focus areas include philosophy of science, mind, ethics, and Asian philosophy.