Portrait of Alekh Agarwal

Alekh Agarwal



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.

I am broadly interested in Machine Learning and Reinforcement Learning. For more detailed information, please visit my personal webpage at http://alekhagarwal.net/.



Project Malmo

Established: June 1, 2015

How can we develop artificial intelligence that learns to make sense of complex environments? That learns from others, including humans, how to interact with the world? That learns transferable skills throughout its existence, and applies them to solve new, challenging problems? https://youtu.be/KkVj_ddseO8 Project Malmo sets out to address these core research challenges, addressing them by integrating (deep) reinforcement learning, cognitive science, and many ideas from artificial intelligence. The Malmo platform is a sophisticated AI experimentation…

Multiworld Testing

Established: November 1, 2013

Exponentially better than A/B testing. Multiworld Testing (MWT) is the capability to test and optimize over K policies (context-based decision rules) using an amount of data and computation that scales logarithmically in K, without necessarily knowing these policies before or during data collection. MWT can answer exponentially more detailed questions compared to traditional A/B testing. The underlying machine learning methodology draws on research on "contextual bandits" and "counterfactual evaluation".…

Explore-Exploit Learning @MSR-NYC

Established: October 24, 2013

This is an umbrella project for machine learning with explore-exploit tradeoff: the trade-off between acquiring and using information. This is a mature, yet very active, research area studied in Machine Learning, Theoretical Computer Science, Operations Research, and Economics. Much of our activity focuses on "multi-armed bandits" and "contextual bandits", relatively simple and yet very powerful models for explore-exploit tradeoff. We are located in (or heavily collaborating with) Microsoft Research New York City. Most of us are…











Alekh Agarwal has been a co-organizer of the NIPS workshop on Optimization for Machine Learning from 2010 through 2015. He has been an area chair or equivalent for ICML (2013, 2015, & 2016), COLT (2013, 2015), AISTATS (2013), and NIPS (2013). He serves as a reviewed for several journals, including JMLR, Annals of Statistics, IEEE Transcations on Automatic Control, IEEE Transcations on Info Theory, SIAM Journal on Optimization, and Machine Learning.