Professor Feng, Weiming

简介

Dr. Weiming Feng is an Assistant Professor at the School of Computing and Data Science, The University of Hong Kong. His research interests are in Theoretical Computer Science, focusing on sampling and approximate counting algorithms for high-dimensional distributions and their applications in Statistics and Learning Theory. He received his Ph.D. in Computer Science from Nanjing University in 2021. Before joining HKU, he held postdoctoral positions at The University of Edinburgh, UC Berkeley, and ETH Zürich.

代表成果

• Weiming Feng, Liqiang Liu, Tianren Liu. On deterministically approximating total variation distance. In Proceedings of the 2024 ACM-SIAM Symposium on Discrete Algorithms (SODA 2024).
• Weiming Feng, Heng Guo, Mark Jerrum, Jiaheng Wang. A simple polynomial-time approximation algorithm for the total variation distance between two product distributions. TheoretiCS, Volume 2 (2023), Article 8, 1-7.
• Weiming Feng, Heng Guo, Chunyang Wang, Jiaheng Wang, Yitong Yin. Towards derandomising Markov chain Monte Carlo. In Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science (FOCS 2023).
• Weiming Feng, Heng Guo, Yitong Yin, Chihao Zhang. Fast sampling and counting k-SAT solutions in the local lemma regime. J. ACM 68(6): 40:1-40:42 (2021).
• Xiaoyu Chen, Weiming Feng, Yitong Yin, Xinyuan Zhang. Rapid mixing of Glauber dynamics via spectral independence for all degrees. In Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science (FOCS 2021).
• Weiming Feng, Nisheeth K. Vishnoi, Yitong Yin. Dynamic sampling from graphical models. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (STOC 2019).