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.
Develop RL methods with rigorous guarantees.
Extract causal information from observational data.