Julie Zhu

I’m a Computational Science and Engineering Ph.D. student at Massachusetts Institute of Technology, working with Professor Youssef Marzouk in the Uncertainty Quantification group. Prior to that, I did my undergrad at NYU Shanghai, where I was fortunate to receive research training under the guidance of Professor Siran Li and Professor Hao Ni. During 2021–2022 I studyed in New York and did research on optimal control and MCMC advised by Professor Georg Stadler and Professor Jonathan Goodman, who led me to the spectacular world of computational math. In 2023, I obtained my B.S. degree (Honors Mathematics and Data Science). During the summer of 2025, I visited UC Berkeley, where I was fortunate to be hosted by Professor Michael Jordan and Dr. Liviu Aolaritei.

My research centers on probabilistic inference and uncertainty quantification, with a focus on structure-exploiting ML for scientific and engineering applications. My current work involves Bayesian/frequentist statistics, sequential data assimilation, optimization, optimal transport, and dimension reduction. I also work on formal mathematical proof with language models and Lean 4, and reinforcement learning for robotics.

If you’re interested in talking about research (or anything more broadly), feel free to send me an email!