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.

My thesis is: Non-convex Bayesian Learning via Stochastic Gradient MCMC

Current interests: Monte Carlo, diffusion models, neural SDE and filtering (with applications in finance), Thompson sampling.

Contact: firstnamelastname056@gmail.com

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News

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!

Dec, 2022. The Contour Sampler is implemented in BlackJAX!

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

Services

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.