Weiming Feng

Simons-Berkeley Research Fellow

UC Berkeley

Photo by Lele Song

Biography

I am Weiming Feng (凤维明), a Simons-Berkeley Research Fellow at the Simons Institute for the Theory of Computing, UC Berkeley. From 2021 to 2023, I was a research associate (postdoc) at the School of Informatics, University of Edinburgh, where I worked in Dr. Heng Guo’s group. I obtained my Ph.D. degree from Nanjing University in June 2021, where I was advised by Prof. Yitong Yin. Before I studied at Nanjing University, I obtained B.Eng. degree from the University of Electronic Science and Technology of China in June 2016, where I majored in Network Engineering.

In Jan 2024, I will be a Junior Fellow at the Institute for Theoretical Studies, ETH Zürich.

My research interest lies in theoretical computer science. Currently, I focus on sampling and counting algorithms. Classic topics include Markov chain Monte Carlo (MCMC) methods, spatial mixing of Gibbs distributions and computational phase transitions. I am also interested in new problems that arose from recent applications, including dynamic and distributed sampling algorithms.

Email: fwm1994 AT gmail DOT com

Here is my CV

Interests

Randomized Algorithms
Discrete Probability

Education

Ph.D. in Computer Science, 2016 - 2021

Nanjing University (NJU)
B.Eng. in Network Engineering, 2012 - 2016

University of Electronic Science and Technology of China (UESTC)

Preprints

Konrad Anand, Weiming Feng, Graham Freifeld, Heng Guo, Jiaheng Wang (2023). Approximate counting for spin systems in sub-quadratic time.

PDF

Publications

Weiming Feng, Heng Guo, Chunyang Wang, Jiaheng Wang, Yitong Yin (2023). Towards derandomising Markov chain Monte Carlo. In FOCS 2023.

PDF Slides@USTC Poster@MIT Heng's slides Heng's talk Jiaheng's slides

Weiming Feng, Heng Guo, Jiaheng Wang (2023). Swendsen-Wang dynamics for the ferromagnetic Ising model with external fields. Information and Computation (IANDC) 294: 105066 (2023).

PDF

Weiming Feng, Heng Guo, Mark Jerrum, Jiaheng Wang (2023). A simple polynomial-time approximation algorithm for the total variation distance between two product distributions. TheoretiCS, Volume 2 (2023), Article 8, 1-7 (conference version in SOSA 2023).

PDF Poster@Zinal Slides@Zinal Slides@PKU Jiaheng's slides

Charilaos Efthymiou, Weiming Feng (2023). On the mixing time of Glauber dynamics for the hard-core and related models on G(n,d/n). In ICALP 2023.

PDF Slides@ICALP

Xiaoyu Chen, Weiming Feng, Yitong Yin, Xinyuan Zhang (2022). Optimal mixing for two-state anti-ferromagnetic spin systems. In FOCS 2022.

PDF Slides@FOCS

Weiming Feng, Heng Guo, Jiaheng Wang (2022). Improved bounds for randomly colouring simple hypergraphs. In RANDOM 2022.

PDF Jiaheng's slides

Weiming Feng, Heng Guo, Yitong Yin, Chihao Zhang (2022). Rapid mixing from spectral independence beyond the Boolean domain. ACM Transactions on Algorithms (TALG) 18(3): 28:1-28:32 (conference version in SODA 2021).

PDF Slides@SODA

Weiming Feng, Heng Guo, Yitong Yin (2022). Perfect sampling from spatial mixing. Random Structures & Algorithms (RSA) 61(4): 678-709.

PDF Slides@E-PIC

Xiaoyu Chen, Weiming Feng, Yitong Yin, Xinyuan Zhang (2021). Rapid mixing of Glauber dynamics via spectral independence for all degrees. In FOCS 2021.

PDF Talk@FOCS Poster@HALG Slides@FOCS Slides@Oxford Slides@UCSB

Weiming Feng, Heng Guo, Yitong Yin, Chihao Zhang (2021). Fast sampling and counting k-SAT solutions in the local lemma regime. Journal of the ACM (JACM) 68 (6), 1-42 (Conference version in STOC 2020).

PDF Talk@STOC Talk@IJTCS Slides@STOC Slides@ICT_CAS Heng's slides Heng's talk

Weiming Feng, Kun He, Yitong Yin (2021). Sampling constraint satisfaction solutions in the local lemma regime. In STOC 2021.

PDF Slides@BARC Kun's talk

Weiming Feng, Nisheeth K. Vishnoi, Yitong Yin (2021). Dynamic sampling from graphical models. SIAM Journal on Computing (SICOMP), 50(2), 350–381. (Confernece version in STOC 2019).

PDF Poster@STOC Slides@STOC Yitong's slides

Weiming Feng, Thomas P. Hayes, Yitong Yin (2021). Distributed Metropolis sampler with optimal parallelism. In SODA 2021.

PDF Slides@SODA Yitong's slides

Weiming Feng, Kun He, Xiaoming Sun, Yitong Yin (2021). Dynamic inference in probabilistic graphical models. In ITCS 2021.

PDF Kun's talk

Weiming Feng, Yuxin Sun, Yitong Yin (2020). What can be sampled locally?. Distributed Computing (DC) 33, 227–253 (conference version in PODC 2017).

PDF Yitong's slides

Weiming Feng, Yitong Yin (2018). On local distributed sampling and counting. In PODC 2018.