UC Berkeley IEOR welcomes new faculty member

Chenyang Zhong joins the department as a new assistant professor. His research develops mathematical and computational methods for stochastic modeling, inference, and optimization, addressing emerging challenges in modern operations research and machine learning.

UC Berkeley’s Department of Industrial Engineering and Operations Research (IEOR) is pleased to welcome Chenyang Zhong as a new assistant professor.

Zhong’s research sits at the intersection of stochastic modeling, sampling, optimization, and machine learning. His work focuses on areas such as optimal transport, generative modeling, Monte Carlo methods, variational inference, and ranking and choice models. Across these areas, he develops theory and computationally efficient methods that help researchers extract insights from complex modern data, quantify uncertainty, and make reliable decisions.

Zhong completed his Ph.D. in Statistics at Stanford University under the guidance of Persi Diaconis, a mathematician known for his contributions to probability, statistics, combinatorics, and Monte Carlo methods. Before joining UC Berkeley, he held a faculty position in the Department of Statistics at Columbia University.

At Berkeley IEOR, Zhong will bring new depth to the department’s work in stochastic modeling, machine learning, and decision-making under uncertainty, contributing methods that can help researchers evaluate competing choices and design reliable, efficient computational tools.