Optimal Scheduling of Elastic and Inelastic Jobs A wide range of modern computer systems rely on parallelism to process jobs quickly. Unlike the jobs considered in most classical scheduling problems, parallelizable jobs can be completed more quickly when they are … Read more

Random growth models and the KPZ universality Many two-dimensional random growth models, including first- and last-passage percolation, are conjectured to fall within the KPZ universality class under mild assumptions on the underlying noise. In recent years, researchers have focused on … Read more

Equations on Wasserstein Space and Applications to Stochastic Mean Field Control and Games The study of controlled multi-agent systems has seen increased interest in recent years, due to their ubiquity in applications coming from macroeconomics, social behavior, and telecommunications. When … Read more

Phase Transitions in Stochastic Many-Player Games My research addresses stochastic games with many players, optimal control with partial information, and, recently, optimization with random matrices. This seminar focuses on a stochastic game where the players exhibit qualitatively distinct behaviors in … Read more

Spherical Spin Glasses and Stochastic Gradient Descent This talk will focus on two strands of my recent research – spin glasses and stochastic gradient descent (SGD). While the contexts are different, both draw on tools from high-dimensional probability to study … Read more

Towards A New Frontier of Trustworthy AI: Interpretable Machine Learning Algorithms that Produce All Good Models Machine learning has been increasingly deployed for high-stakes decisions that deeply impact people's lives. My research focuses on developing interpretable algorithms and pipelines to … Read more

Phase Transitions of Random Constraint Satisfaction Problems The framework of constraint satisfaction problems (CSPs) captures many fundamental problems in combinatorics and computer science, such as finding a proper coloring of a graph or solving Boolean satisfiability problems. To study the … Read more

New Perspectives on Data-Driven Optimization and Neural Network Uncertainty Quantification I will talk about some recent methodologies to understand and quantify the impact of data in relation to optimization and simulation. In the first part of the talk, we create … Read more

Coalescent Theory and its Applications in Population Genomics As we gain access to more datasets with finer resolution, what assumptions should we make in our models? Idealized models in population genetics, which often lack spatial structure and other realistic features … Read more