Sham M. Kakade
Principal Research Scientist
The focus of my work is on designing scalable and efficient algorithms for machine learning and artificial intelligence.
I have worked on problems in unsupervised (and representational) learning, algorithmic statistics, probabilistic inference, reinforcement learning, statistical learning theory, game theory, and economics. As a graduate student, I focused on reinforcement learning and computational neuroscience. My thesis was on sample complexity issues in reinforcement learning.
I am a principal research scientist at Microsoft Research, New England, a lab in Cambridge, MA. Previously, I was an associate professor at the Department of Statistics, Wharton, University of Pennsylvania (from 2010-2012), and I was an assistant professor at the Toyota Technological Institute at Chicago. Before this, I did a postdoc in the Computer and Information Science department at the University of Pennsylvania under the supervision of Michael Kearns. I completed my PhD at the Gatsby Unit where my advisor was Peter Dayan. Before Gatsby, I was an undergraduate at Caltech where I did my BS in physics.
Activities and Services
Program committee for the third New England Machine Learning Day, May 13th, 2014.
Co-chair for New York Computer Science and Economics Day V, Dec 3rd, 2012.
Program committee for the first New England Machine Learning Day, May 16th, 2012.
Program chair for the 24th Annual Conference on Learning Theory (COLT 2011) which took place in Budapest, Hungary, on July 9-11, 2011.
Former Interns (in reverse chronological order)
Email: skakade [at] microsoft [dot] com