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
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
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
I am very open to new collaborations.
If you are interested in machine learning, optimization,
statistics, CS theory, or related areas, please apply either through the
computer science website
statistics website. UW
is a great place for these activities.
Some good stuff at UW:
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.
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
CSE 546: Machine Learning
Stat 928: Statistical Learning Theory
Stat 991: Multivariate
Analysis, Dimensionality Reduction, and Spectral Methods
Large Scale Learning
Gabriel Cadamuro (co-advised with
Daniel Hsu (while at UPenn)
(in reverse chronological order)
Daniel Hsu (while at TTI-C)
Sathyanarayan Anand (while at TTI-C)
Email: sham [at] cs [dot] washington [dot] edu
Department of Statistics, Office 303
Computer Science & Engineering, Office 436
Paul Allen Center
University of Washington
Seattle, WA 98195