Weiming Feng

Research Fellow, UC Berkeley

Weiming Feng is a research associate (postdoc) in the School of Informatics, University of Edinburgh. He obtained his PhD degree from Nanjing University in June 2021, where his advisor is Professor Yitong Yin. His research interest lies in theoretical computer science. Currently, he focuses on sampling and counting algorithms. Classic topics include Markov chain Monte Carlo (MCMC) methods, spatial mixing of Gibbs distributions and computational phase transitions. He is also interested in new problems that arose from recent applications, including dynamic and distributed sampling algorithms.

Program Visits

Sampling/Counting Algorithms; Markov chain Monte Carlo