Sham M. Kakade

Principal Research Scientist
Microsoft Research, New England


This fall I will be joining the University of Washington as a Washington Research Foundation Data Science Chair, with a joint appointment in the Department of Statistics and the Department of Computer Science .


New England Machine Learning Day 2015
The fourth annual New England Machine Learning Day will be held May 18th, 2015. The event will bring together local academics and researchers in machine learning and its applications. There will be a lively poster session during lunch, like in previous years.
IMS-MSR Workshop: Foundations of Data Science
The goal of this workshop in June 2015 is to foster the communication of communities broadly working in the area of data science, with a particular focus of stimulating increased interactions between statisticians, computer scientists, and domain experts in order to ambitiously attack important scientific problems involving big and complex data.



See my Official Bio for publicity purposes.
I am 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 PhD was at the Gatsby Computational Neuroscience Unit under the supervision of Peter Dayan. I was an undergraduate at Caltech where I obtained my BS in physics.


My work is in the area broadly construed as data science, focusing on large scale computational methods for statistics, machine learning, 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.


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

Course Links

Stat 928: Statistical Learning Theory
Stat 991: Multivariate Analysis, Dimensionality Reduction, and Spectral Methods
Large Scale Learning
Learning Theory

Former Postdocs

Daniel Hsu (while at UPenn)

Former Interns (in reverse chronological order)

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: skakade [at] microsoft [dot] com

Microsoft Research, Office 14060
One Memorial Drive
Cambridge, MA 02142