Sham M. Kakade
Principal Research Scientist 

PublicationsOfficial BioBioI 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 20102012), 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. ResearchThe focus of my work is on designing (and implementing) both statistically and computationally efficient algorithms for machine learning, statistics, and artificial intelligence. Recently, I have been focusing on three areas: 1) designing effective algorithms for estimating probabilistic models with latent structure (such as HMMs, LDA, Mixtures of Gaussians, etc) 2) efficient optimization algorithms in statistical settings (i.e. how fast can we optimize when we are interested in statistical accuracy rather than numerical accuracy?) 3) what is responsible for the recent (and remarkable) successes in deep learning? (this last question is largely an empirical exploration, in both computer vision and speech applications).Recently, I have been focusing on three areas: 1) designing effective algorithms for estimating probabilistic models with latent structure (such as HMMs, LDA, Mixtures of Gaussians, etc) 2) efficient optimization algorithms in statistical settings (i.e. how fast can we optimize when we are interested in statistical accuracy rather than numerical accuracy?) 3) what is responsible for the recent (and remarkable) successes in deep learning? (this last question is largely an empirical exploration, in both computer vision and speech applications). More broadly, I am currently interested in probability theory, algebraic and tensor methods, signal processing/information theory, and numerous domain specific settings (with a recent focus on natural language processing and computer vision). My previous body of work has addressed problems in unsupervised (and representational) learning, concentration of measure, reinforcement learning, statistical learning theory, optimization, algorithmic 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. TutorialsTensor Decompositions for Learning Latent Variable Models, AAAI 2014 Tensor Decompositions Methods for Learning Latent Variable Models, ICML 2013 Course LinksStat 928: Statistical Learning Theory Stat 991: Multivariate Analysis, Dimensionality Reduction, and Spectral Methods Large Scale Learning Learning Theory Former PostdocsDaniel Hsu (while at UPenn) Former Interns (in reverse chronological order)Aaron Sidford Roy Frostig David Belanger Chen Wang Qingqing Huang Jaehyun Park Karl Stratos Dokyum Kim Praneeth Netrapalli Rashish Tandon Rong Ge Adel Javanmard Matus Telgarsky Daniel Hsu (while at TTIC) Sathyanarayan Anand (while at TTIC) Contact InfoEmail: skakade [at] microsoft [dot] com 