Broadly speaking, my research interests revolve around Machine Learning, Artificial Intelligence, Statistics, and Theoretical Computer Science. I am interested in formulating new/emerging learning scenarios (including various forms of unsupervised learning), and providing provably efficient methods for — or establishing inherent limitations in — solving them.
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Hassan Ashtiani is an Assistant Professor in the Department of Computing and Software at McMaster University, and a faculty affiliate at Vector institute. He obtained his Ph.D. in Computer Science in 2018 from University of Waterloo where he was advised by Shai Ben-David. Before that, he received his master’s degree in AI and Robotics and his bachelor’s degree in Computer Engineering, both from University of Tehran. Broadly speaking, a major theme in his research is the design and analysis of sample-efficient learning algorithms. In recent years, he has focused on studying sample-efficient learning methods that are robust to (i) model misspecification, (ii) distribution shift, (iii) adversarial attacks, and/or (iv) privacy-related attacks.
Related Courses
The main focus of the course is on the modern aspects of distribution learning. The topics include PAC learning of distributions, efficient learning of mixture models, information theoretic aspects of distribution learning, computational complexity distribution learning, models for robust distribution learning, differentially private distribution learning, open questions in modern distribution learning, generative adversarial networks, and variational autoencoders. The course will be mostly based on recent papers, but some introductory aspects can be found in the book titled “Combinatorial Methods in Density Estimation” by Luc Devroye and Gabor Lugosi.
