Statistical Learning, Reinforcement Learning, Nonconvex and Stochastic Optimization, Applied Probability

Algorithms for combinatorial, stochastic, and online optimization

Large-scale optimization algorithms, lower bounds in optimization, optimization in ML

Computational complexity of statistical inference, mathematical statistics, applied probability, and information theory

Decision-making under uncertainty, game theory and mechanism design for data marketplaces

Theory of Metalearning, Learning-augmented algorithms

Data processing, data pricing, and managing data under uncertainty

Computability theory and applications to model theory

Discrete probability, nonconvex optimization, with particular interest in matrix factorization

Combinatorial optimization, polyhedra, game theory

Theory and practice of large-scale machine learning systems

Applied probability, statistics

Theory, algorithms, implementation and applications of optimization

• abtin@cs.wisc.edu

• jiaqicheng@cs.wisc.edu

• iakovidis@wisc.edu

• mingchen@cs.wisc.edu

• amaran@cs.wisc.edu

• jmcmahan@wisc.edu

• dbpatel5@wisc.edu

• pittas@wisc.edu

• sdroievski@wisc.edu

• ihm2@wisc.edu

• su228@wisc.edu

• zztang@wisc.edu

• pxiong79@wisc.edu

• saikumar@cs.wisc.edu

• benyoung@cs.wisc.edu

• zarifis@wisc.edu