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Seminar on Control Theory and Nonlinear Filtering
Seminar on Control Theory and Nonlinear Filtering
Fast, Multimodal, Derivative-Free Bayes Inference with Fisher-Rao Gradient Flows
Fast, Multimodal, Derivative-Free Bayes Inference with Fisher-Rao Gradient Flows
Organizer
Stephen S-T. Yau
Speaker
Jiayi Kang
Time
Wednesday, October 2, 2024 8:00 PM - 9:00 PM
Venue
Online
Abstract
In this group meeting I will report two papers, 1 Sampling via gradient flows in the space of probability measures. https://arxiv.org/abs/2310.03597 2 Efficient, multimodal, and derivative-free Bayesian inference with Fisher-Rao gradient flows. https://arxiv.org/abs/2406.17263
Speaker Intro
Jiayi Kang received his Ph.D. in Mathematics from Tsinghua University in 2024. He joined the Beijing Institute of Mathematical Sciences and Applications (BIMSA) as an Assistant Researcher in July 2024, and became an Assistant Professor at the Hetao Institute for Mathematical and Interdisciplinary Sciences (HIMIS) in November 2025.
His research focuses on the intersection of deep learning, nonlinear filtering, and computational biology. His main research interests include: neural network-based filtering algorithms and their mathematical foundations, sampling methods in Wasserstein geometry, nonlinear filtering theory (including the Yau-Yau method) and its applications in climate science and other fields, as well as computational genomics and evolutionary system modeling. He is committed to solving complex problems in science and engineering using mathematical and machine learning methods.