Portrait of Miro Dudík

Miro Dudík

Senior Researcher


Miro Dudík’s research focuses on combining theoretical and applied aspects of machine learning, statistics, convex optimization and algorithms. Most recently he has worked on contextual bandits, large-scale learning, and tractable pricing of prediction markets.

He received his PhD from Princeton in 2007. He is a co-creator of the MaxEnt package for modeling species distributions, which is used by biologists around the world to design national parks, model impacts of climate change, and discover new species.


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…