Tamir Hazan, Ph.D.
Faculty of Industrial Engineering and Management
Our research interests involve the theoretical and practical aspects of machine learning. Our research focuses on mathematically founded solutions to modern real life problems that demonstrate non-traditional statistical behavior. Recent examples are perturbation models that allow efficient learning of high dimensional statistics, deep learning of infinite networks and primal-dual optimization for high-dimensional inference problem. The practice of our work is motivated by many computer vision problems, as well as computational biology and language processing.
Alex Schwing, now asisstant professor at UIUC.
Alon Cohen, (Ph.D. student)
Sergey Voldman, (Ph.D. student, jointly with prof. Shoham Sabach)
Andrey Isakov, (M.Sc. student)
Idan Schwartz, (M.Sc. student, jointly with prof. Benny Kimelfeld)
Noam Heimann, (M.Sc. student, jointly with prof. Erez Karpas)
Perturbations, Optimization, and Statistics (2016)
Tamir Hazan, George Papandreou, Daniel Tarlow (Editors).
Neural Information Processing series, MIT Press.
[MIT Press], [amazon]