Algorithmic Foundations of Data Science Institute
UW got a NSF Tripods grant! We started an institute on the theoretical
foundations of data science. Also, we received more
our data science efforts.
I study the theoretical foundations of machine learning and AI in a
broad range of paradigms, and I enjoy collaborating with a diverse
set of researchers to tackle these issues.
Understanding these foundations is central in our design of
algorithms which are efficient from both computational and statistical
perspectives. I seek to use these advancements to help in furthering
the state of the art in various AI domains.
Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in
of Computer Science and the
at the University of
He works on the theoretical foundations of machine learning, focusing
on designing provable and practical statistically and computationally
efficient algorithms. Amongst his contributions, with a diverse set of
collaborators, are: establishing principled approaches in
reinforcement learning (including the natural policy gradient, conservative
policy iteration, and the PAC-MDP framework); optimal algorithms in
the stochastic and non-stochastic multi-armed bandit problems
(including the widely used linear bandit and the Gaussian process
bandit models); computationally and statistically efficient
spectral algorithms for estimation of latent variable models
(including estimation of mixture of Gaussians, latent
Dirichlet allocation, hidden markov models, and overlapping
communities in social networks); faster algorithms for large
scale convex and nonconvex optimization (including how to escape
from saddle points efficiently). He is the
recipient of the IBM Goldberg best paper award (in 2007) for
contributions to fast nearest neighbor search and the best
paper, INFORMS Revenue Management and Pricing Section Prize
(2014). He has been program chair for COLT 2011.
Sham completed his Ph.D. at the Gatsby Computational Neuroscience Unit
at University College London, under the supervision of Peter
and he was a postdoc at the University of Pennsylvania, under the
supervision of Michael Kearns. Sham was an undergraduate at
Caltech, studying in physics under the supervision of John Preskill.
Sham has been a Principal Researcher at
Microsoft Research, New England, an associate professor at the
Department of Statistics, Wharton, UPenn, and an assistant professor
at the Toyota Technological Institute at Chicago.
Activities and Services
Committee for the
Sloan Research Fellowships in Computer Science (active).
Co-organizer for the
Simons Foundations of Machine Learning, Winter, 2017
Co-chair for the
Simon's Representational Learning workshop, March, 2017
Co-chair for the
IMS-MSR Workshop: Foundations of Data Science, June 11th, 2015.
Steering committee for the fourth
New England Machine Learning Day, May 18th, 2015.
Program committee for the third
New England Machine Learning Day, May 13th, 2014.
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.
Tensor Decompositions for Learning Latent Variable Models, AAAI 2014
Tensor Decompositions Methods for Learning Latent Variable Models, ICML 2013
CSE 547 / STAT 548: Machine Learning for Big Data, Spring 2018
CSE 446: Machine Learning, Winter 2018
CSE 547 / STAT 548: Machine Learning for Big Data, Spring 2017
CSE 546: Machine Learning, Autumn 2016
CSE 547 / STAT 548: Machine Learning for Big Data, Spring 2016
CSE 546: Machine Learning, Autumn 2015
Stat 928: Statistical Learning Theory
Stat 991: Multivariate
Analysis, Dimensionality Reduction, and Spectral Methods
Large Scale Learning
Ramya Korlakai Vinayak
Gabriel Cadamuro (co-advised with
(in reverse chronological order)
Daniel Hsu (while at TTI-C)
Sathyanarayan Anand (while at TTI-C)
Email: sham [at] cs [dot] washington [dot] edu
CSE office address:
Computer Science & Engineering, Office 436
Paul Allen Center
Stat office address:
Department of Statistics, Office 303
University of Washington
Seattle, WA 98195