Sham M. Kakade

Washington Research Foundation Data Science Chair

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

Senior Data Science Fellow,
eScience Institute

Adjunct Professor in:
Department of Electrical Engineering

University of Washington


News:

UW got an NSF Tripods grant!
We are starting an institute on the theoretical foundations of data science. More info here.

MusicNet!  


Publications  


Research

My research focuses on the rigorous foundations of statistical and computational learning in a diverse set of paradigms, ranging from neuroscience (my graduate work started with examining the neural and psychological basis of animal and human learning) to machine learning (and reinforcement learning) to learning in game-theoretic and economic settings.
Machine learning is enjoying tremendous advances, and my group's research objectives are to aid in these collective efforts to rapidly advance progress on core scientific, technological, and AI problems. Towards these ends, much of my work is in the balance between provably efficient methods (both from a statistical and computational perspective) and practically effective methods. Some of my recent research interests are:
  • pratical and provably efficient methods for large scale optimization, which address statistical issues and non-convexity (see here, here and here).
  • learning in structured settings, such as estimating latent variable models, memory models, etc. (see here, here, and here).
  • specific application domains (e.g. music and robotics).
I enjoy collaborating with researchers, across a variety of different areas (including statistics, computer science, signal processing, AI application domains, social sciences, economics, psychology, and biology/neuroscience).

About me:

Sham Kakade is a Washington Research Foundation Data Science Chair, with a joint appointment in the Department of Statistics and the Department of Computer Science at the University of Washington.
From 2011-2015, I was a principal research scientist at Microsoft Research, New England. From 2010-2012, I was an associate professor at the Department of Statistics, Wharton, University of Pennsylvania. From 2005-2009, I was an assistant professor at the Toyota Technological Institute at Chicago.
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. I was a postdoc in the Computer and Information Science department at the University of Pennsylvania under the supervision of Michael Kearns.

Some Activities and Services

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.
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.

Tutorials

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

Course Links

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
Learning Theory

Current Postdocs

Ramya Korlakai Vinayak

Current Students

Gabriel Cadamuro (co-advised with Josh Blumenstock)
Rahul Kidambi
Krishna Pillutla (co-advised with Zaid Harchaoui)
Aravind Rajeswaran (co-advised with Emo Todorov)
John Thickstun (co-advised with Zaid Harchaoui)

Former Postdocs

Praneeth Netrapalli
Rong Ge
Daniel Hsu

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

CSE office address:
Computer Science & Engineering, Office 436
Paul Allen Center

Stat office address:
Department of Statistics, Office 303
Padelford Hall

University of Washington
Seattle, WA 98195