Mohammad Ghavamzadeh
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Description
Mohammad Ghavamzadeh received a Ph.D. degree in computer science from the University of Massachusetts Amherst in 2005. Since September 2005 he has been a postdoctoral fellow at the Department of Computing Science at the University of Alberta, working with Prof. Richard Sutton. The main objective of his research is to investigate the principles of scalable decision-making grounded by real-world applications. In the last two years, Ghavamzadeh’s research has been mostly focused on using recent advances in statistical machine learning, especially Bayesian reasoning and kernel methods, to develop more scalable reinforcement learning algorithms.
Lectures:
tutorial![]() as author at Tutorials, 16148 views |
poster![]() as author at New Frontiers in Model Order Selection, together with: Alexandre Lacoste, Nicolas Baskiotis, Stefan Kremer, Aurélie Boisbunon, Morteza Haghir Chehreghani, Yuri Grinberg, Amir-massoud Farahmand, Marina Sapir, Yevgeny Seldin, 4363 views |
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lecture![]() as author at Large-scale Online Learning and Decision Making (LSOLDM) Workshop, Cumberland Lodge 2012, 3571 views |
lecture![]() as author at Cognitive Systems Workshop & Thematic Programmes and Pump Priming Workshops, Cumberland Lodge 2012, 3672 views |
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demonstration video![]() as author at Video Journal of Machine Learning Abstracts - Volume 1, 3086 views |
lecture![]() as author at Deep Learning and Reinforcement Learning Summer School, Toronto 2018, 1292 views |
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