معرفی
Dr. Weiming Feng is an incoming Assistant Professor at the School of Computing and Data Science of The University of Hong Kong. His research focuses on Theoretical Computer Science, particularly sampling and approximate counting algorithms, with applications in Statistics and Learning Theory. Prior to joining HKU, he held postdoctoral positions at The University of Edinburgh, UC Berkeley, and ETH Zürich.
Education: PhD in Computer Science from Nanjing University (2021).
Research interests include Discrete Probability, Markov Chain Monte Carlo (MCMC), and algorithmic developments for high-dimensional distributions. His work bridges theoretical foundations and practical applications, addressing challenges in computational efficiency and probabilistic modeling.
Notable contributions include advancements in deterministic approximation of statistical distances, MCMC derandomization, and fast sampling techniques for combinatorial problems. His publications span top venues like SODA, FOCS, and STOC, reflecting contributions to algorithm design and computational theory.
Lab and Team: While specific lab affiliations are not mentioned, his research is embedded within the broader computational and data science initiatives at HKU's School of Computing and Data Science.
