Alekh Agarwal

Researcher
Microsoft Research, New York
Email:


About Me

I am currently a researcher in the New York lab of Microsoft Research, where I also spent two wonderful years as a postdoc. Prior to that, I obtained my PhD in Computer Science from UC Berkeley, working with Peter Bartlett and Martin Wainwright.
This webpage is no longer being actively maintained. For the updated version, please visit my personal webpage here.

Interests
I am broadly interested in Machine Learning, Statistics and Optimization. My research focus is on problems which arise while applying machine learning techniques to massive datasets. Part of my research aims to understand the tradeoffs between learning and computation, as well as designing efficient learning algorithms that can learn under a given computational budget. On the algorithmic side, I am also quite interested in the design of distributed machine learning algorithms. Some of my other work considers computational and statistical aspects of estimation in high-dimensional problems. More recently, I have been looking at approaches for learning feature representations from data, in a theoretically principled and practically efficient manner. In a past life, I worked on Machine Learning applied to Web Search and Ranking.

Publications

Ph.D. Thesis
Preprints Journal Publications Conference Publications

Professional Activities

Fundraising Chair for AISTATS 2016.
Co-organized NIPS 2015 workshop on Optimization for Machine Learning.
Co-organized NIPS 2014 workshop on Optimization for Machine Learning.
Co-organized NIPS 2013 workshop on Optimization for Machine Learning.
Co-organized NIPS 2013 workshop on Optimization for Machine Learning.
Co-organized NIPS 2012 workshop on Optimization for Machine Learning.
Co-organized NIPS 2011 workshop on Computational Trade-offs in Statistical Learning.
Co-organized NIPS 2010 workshop Learning on Cores, Clusters and Clouds.
Area chair or equivalent: NIPS 2016, ICML 2016, ICML 2015, COLT 2015, ICML 2013, COLT 2013, AISTATS 2013, NIPS 2013.
Journal Reviewing: JMLR, Annals of Statistics, IEEE Transcations on Automatic Control, IEEE Transcations on Info Theory, SIAM Journal on Optimization, Machine Learning.

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