Wei Deng

I am a ML Researcher at Morgan Stanley. My aim is to build scalable and reliable probabilistic methods 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, deep state-space/ filtering methods (applicable to finance), Thompson sampling.

Contact: firstnamelastname056@gmail.com


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 Opt and ML Seminar at HKU

Oct, 2021. I have defended my thesis!

Oct, 2020. Talk at ML + 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.