Weiming Feng

Professor Weiming Feng is an Assistant Professor in the School of Computing and Data Science at The University of Hong Kong. His research lies in theoretical computer science, with a primary focus on sampling and counting algorithms. He studies fundamental algorithmic questions related to Markov chain Monte Carlo (MCMC) methods, spatial mixing properties of Gibbs distributions, and computational phase transitions, as well as their applications in statistics and learning theory. Professor Feng received his Ph.D. from Nanjing University in 2021 under the supervision of Professor Yitong Yin. Prior to joining HKU, he held postdoctoral positions at the Institute for Theoretical Studies at ETH Zürich, the Simons Institute at UC Berkeley, and the School of Informatics at the University of Edinburgh. His recent work has led to advances in rapid mixing of Markov chains, coupling and entropic independence techniques, and efficient algorithms for counting and approximation, with publications in leading theory venues such as FOCS, SODA, COLT, ITCS, RANDOM, and SoCG.