Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in the Department of Statistics and the Department of Computer Science at the University of Washington. Kakade works in the area broadly construed as data science, focusing on large-scale computational methods for machine learning, statistics, and signal processing. The hope is to see these tools advance the state of the art on core scientific, technological, and AI problems in the near future. Kakade enjoys collaborating with applied and theoretical researchers, across a variety of different areas. Kakade is actively working on various theoretical and applied questions. Some of his recent theoretical work focuses on developing computationally efficient algorithms (both provably so and in practice) for large-scale statistical estimation problems (such as those with latent structure). With various collaborators, Kakade has also been actively working on applied problems in computer vision, music, and natural language processing, where his goal is both to advance the state of the art and to address novel challenges.
- Foundations of Machine Learning, Spring 2017. Visiting Scientist, Program Organizer and Workshop Organizer.