Baojian Zhou (周宝健) — Assistant Professor, School of Data Science, Fudan University

I am an assistant professor in the School of Data Science at Fudan University, where I am also affiliated with the Shanghai Key Laboratory of Data Science and the Knowledge Works Research Laboratory. Before joining Fudan, I was a postdoctoral researcher at Stony Brook University with Steven Skiena from 2020 to 2021. I received my PhD in Computer Science and my MA in Mathematics from the University at Albany, SUNY, where I was fortunate to be advised by Feng Chen and Yiming Ying.

My research is in machine learning and mathematical optimization. Much of my work concerns fast algorithms for large-scale graphs — locally evolving set processes, personalized PageRank, online node labeling, and graph-structured sparse optimization — where the aim is to compute what you need while touching as little of the graph as possible. More recently I have been working on the mathematical foundations of large language models: in-context learning, and the training dynamics and trainability of diffusion language models. I am also interested in applying these methods to scientific problems, from the statistical structure of language evolution to AI for Science more broadly.