Computational Complexity of Statistical Inference

Explore computational vs. statistical tradeoffs in estimation problems.

Networks and Graphical Models

Model and predict complex interactions.

Geometric Aspects of Sampling and Optimization

Develop a combined perspective that leads to simultaneous progress on both optimization and sampling.

Machine Learning for Algoritms

Leverage machine learning to improve the performance of classical algorithms.

Sketching, Sampling, and Sublinear-Time Algorithms

Develop techniques that provide tradeoffs between algorithmic efficiency quality of approximation or statistical guarantees for the downstream applications.

Economics and Learning

Develop the theoretical and algorithmic foundations of systems with strategic agents.

Reinforcement Learning

Develop RL methods with rigorous guarantees.

Causal Inference

Extract causal information from observational data.