February: I am co-organizing a minisymposium at SIAM UQ 2024 titled Optimal Transport for Uncertainty Quantification with Panagiota Birmpa. I will also be presenting in the Computational Transport minisymposium where I will be presenting on our recent work relating mean-field games with generative modeling.
I am excited to announce our new preprint title Wasserstein proximal operators describe score-based generative models and resolve memorization. We that score-based generative models can be fundamentally understood as the Wasserstein proximal operator of cross-entropy and we build informed models that resolve the memorization phenomenon in SGMs. This is joint work with Siting Liu, Wuchen Li, Markos Katsoulakis, and Stan Osher.
November: I will be giving a talk at NYU Shanghai on our recent work Mean-Field Games Laboratory for Generative Modeling.
October: I will be visiting Emory University and speaking in their Computational and data-enabled science seminar series.
I gave a talk on our work Mean-Field Games Laboratory for Generative Modeling to the Machine Learning and Mean Field Games Seminar series
June: I gave a talk on our work Mean-Field Games Laboratory for Generative Modeling to the UCLA Level Set Collective.
May: I will be attending the Optimal transport in Data Science at ICERM. I will be presenting a poster on our recent work on the Mean-Field Games Laboratory for Generative Modeling.
April: Excited to announce new preprint titled A Mean-Field Games Laboratory for Generative Modeling. This joint work with Markos Katsoulakis. We show that flow and diffusion-based generative models, including normalizing flows, score-based models, and Wasserstein gradient flows can be derived from a single unifying mean-field games framework.
February: I am the creator and organizer of the Learning Learning. This is a seminar for students and postdocs to present their in-progress research, and practice giving research presentations.
Announced new preprint titled Transport map unadjusted Langevin algorithms. This joint work with Youssef Marzouk and Konstantinos Spiliopoulos. We show that Langevin algorithms applied to target distributions that are preconditioned with a normalizing transport map can accelerate sampling and are related to reversible perturbations of Langevin dynamics.
September: I will be presenting at SIAM MDS 2022 in the minisymposium on Frontiers in Monte Carlo Methods for Physics. My talk will be about our work on the Transport map unadjusted Langevin algorithm.
Our paper on Geometry-informed irreversible perturbations for accelerated convergence of Langevin dynamics was accepted for publication in Statistics and Computing.
I joined the Department of Mathematics and Statistics at UMass Amherst as a postdoctoral research associate.
April: I am co-organizing a minisymposium at SIAM UQ 2022 titled Data-Driven Approaches to Rare and Extreme Events, in which I will be presenting our work on Data-driven methods for rare event simulation in stochastic dynamical systems.
February: Attending the Data Assimilation - Mathematical Foundations and Applications workshop at the Mathematical Research Institute of Oberwolfach, February 20-26.
January: Our paper on A Koopman framework for rare event simulation in stochastic differential equations was accepted for publication in the Journal for Computational Physics.
December I successfully defended my PhD thesis on December 13!