Hi, I’m Bohan Zhou

I’m a visiting assistant professor at the UCSB fortunately mentored by Matt Jacobs.

I am an applied mathematician with interests in developing provable and practical methodologies for analyzing data distributions. My research mostly originates from real-world problems, following channels created by optimal transport (OT) theory, it fluxes into scientific computation, calculus of variations, optimization, network theory, fluid dynamics and data science.

I received my Ph.D. at UC Davis advised by Qinglan Xia. After that, I was a Byrne Instructor in Applied Mathematics at Dartmouth College, in the group of Anne Gelb partially funded by the ONR-MURI program.

Bohan Zhou

ArXiv Google Scholar

Sobolev Gradient Ascent for Optimal Transport: Barycenter Optimization and Convergence Analysis Kaheon Kim, Bohan Zhou, Changbo Zhu, Xiaohui Chen.
(Submitted) 2025. Accelerated Markov Chain Monte Carlo Algorithms on Discrete States Bohan Zhou, Shu Liu, Xinzhe Zuo, Wuchen Li.
(Submitted) 2025. Efficient and Exact Multimarginal Optimal Transport with Pairwise Costs Bohan Zhou, Matt Parno.
(Published) 2022. The existence of minimizers for an isoperimetric problem with Wasserstein penalty term in unbounded domain Qinglan Xia, Bohan Zhou.
(Published) 2020.

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(Signed) Wasserstein Barycenter

How should the mean of a collection of probability distribution be computed in practice? What can we build on the mean?

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Accelerated Sampling

How can we design an accelerated MCMC sampling algorithm on discrete state spaces such as graphs?

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Multimarginal Optimal Transport

Given a sequence of data, what is their distributional representation in the sense of optimal transport theory?

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