Sham M. Kakade

Washington Research Foundation Data Science Chair

Senior Data Science Fellow,
UW eScience Institute.

Associate Professor in both:
Department of Statistics
Department of Computer Science

University of Washington


Foundations of Machine Learning 2017
We are putting together a machine learning semester at the Simons Institute in Berkeley from Jan to May, 2017. We are seeking to bring together a range of researchers to help develop novel and effective algorithms.



See my Official Bio for publicity purposes.
Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in both the Department of Statistics and the Department of Computer Science at the University of Washington.
I completed my PhD at the Gatsby Computational Neuroscience Unit under the supervision of Peter Dayan, and I was an undergraduate at Caltech where I obtained my BS in physics. Before joining UW, I was a principal research scientist at Microsoft Research, New England. Previous to this, 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 (from 2005-2009). Before this, I was a postdoc in the Computer and Information Science department at the University of Pennsylvania under the supervision of Michael Kearns.


My work is 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. I enjoy collaborating with applied and theoretical researchers, across a variety of different areas (including statistics, computer science, signal processing, economics, psychology, and biology/neuroscience).
I am actively working on various theoretical and applied questions. Some of my recent theoretical work focusses 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, I have also been actively working on applied problems in both computer vision and natural language processing (and, to a lesser extent, computational biology and speech recognition), where our goal is to advance the state of the art. Part of these latter efforts have involved empirical studies of deep learning methods (as some of these methods have recently achieved remarkable empirical successes).
I am very open to new collaborations.

Prospective Students

If you are interested in machine learning, optimization, statistics, CS theory, or related areas, please apply either through the computer science website or statistics website. UW is a great place for these activities.

Some good stuff at UW:

Data Science
Machine Learning
CS Theory
Artificial Intelligence

Some Older Activities and Services

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.
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.


Tensor Decompositions for Learning Latent Variable Models, AAAI 2014
Tensor Decompositions Methods for Learning Latent Variable Models, ICML 2013

Course Links

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
Learning Theory

Current Students

Gabriel Cadamuro (co-advised with Josh Blumenstock)
Rahul Kidambi
John Thickstun (co-advised with Zaid Harchaoui)

Former Postdocs

Daniel Hsu (while at UPenn)
Karthik Sridharan

Former Interns (in reverse chronological order)

Chi Jin
Aaron Sidford
Roy Frostig
David Belanger
Chen Wang
Qingqing Huang
Jaehyun Park
Karl Stratos
Do-kyum Kim
Praneeth Netrapalli
Rashish Tandon
Rong Ge
Adel Javanmard
Matus Telgarsky
Daniel Hsu (while at TTI-C)
Sathyanarayan Anand (while at TTI-C)

Contact Info

Email: sham [at] cs [dot] washington [dot] edu

Department of Statistics, Office 303
Padelford Hall

Computer Science & Engineering, Office 436
Paul Allen Center

University of Washington
Seattle, WA 98195