Sham Kakade

Sham Kakade is a Professor of Computer Science and Professor of Statistics Co-director at the Kempner Institute for the Study of Natural & Artificial Intelligence at Harvard University's John A. Paulson School of Engineering and Applied Sciences. His research areas include Applied Mathematics, Machine Learning, and Artificial Intelligence. Kakade's work focuses on advancing computational methods for complex systems. He is affiliated with the Department of Computer Science and maintains a profile page at https://seas.harvard.edu/person/sham-kakade. His contact information is sham@seas.harvard.edu.

Sham M Kakade is a researcher at Harvard University specializing in Machine Learning, Artificial Intelligence, Statistics, and Optimization. His work focuses on developing algorithms for reinforcement learning, optimization under bandit feedback, and statistical methods for latent variable models. He has contributed to areas such as Gaussian process optimization, policy gradient methods, and meta-learning with implicit gradients. His research also explores topics like stochastic linear optimization, multi-view clustering, and the theoretical foundations of learning and selective attention. Kakade's publications emphasize the intersection of statistics, optimization, and machine learning, with a focus on regret bounds, experimental design, and efficient learning algorithms.