I am a Machine Learning Researcher at Morgan Stanley. My aim is to advance probabilistic methods for more reliable AI across supervised, generative, and time-series domains. I got my Ph.D. in applied math at Purdue University in Dec 2021, where I was advised by Prof. Guang Lin and Faming Liang.
Current interests: Monte Carlo, diffusion models, neural SDE and filtering (with applications in finance), Thompson sampling.
Dec, 2023. Glad to participate Transport, Diffusion, and Sampling Workshop
Nov, 2023. Talk at Financial Mathematics Seminar at FSU
Apr, 2023. NewBeginnings: One ICML on diffusion models and Schrodinger bridge is accepted!
Feb, 2022. Talk at Machine Learning Seminar at HKU
Oct, 2021. I have defended my thesis!
Oct, 2020. Talk at Machine Learning + X Seminar at Brown University
Reviewers: ICML, NeurIPS, ICLR, AISTAT, AAAI, IJCAI, Machine Learning Journal.
La pensée n’est qu’un écliar au milieu d’une longue nuit. Mais c’est cet éclair qui est tout.