Weiming Feng

Weiming Feng

Research Associate

University of Edinburgh

Photo by Hongxun Wu


I am Weiming Feng (凤维明), a research associate (postdoc) in the School of Informatics, University of Edinburgh. I obtained my Ph.D. degree from Nanjing University in June 2021, where I was advised by Professor 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, wfeng AT ed DOT ac DOT uk

Here is my CV

  • Randomized Algorithms
  • Discrete Probability
  • 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)


(2022). Optimal mixing for two-state anti-ferromagnetic spin systems. In FOCS 2022.


(2022). Improved bounds for randomly colouring simple hypergraphs. In RANDOM 2022.

PDF Jiaheng's slides

(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).


(2022). Perfect sampling from spatial mixing. Random Structures & Algorithms (RSA) 61(4): 678-709.


(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

(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

(2021). Distributed Metropolis sampler with optimal parallelism. In SODA 2021.

PDF Slides@SODA Yitong's slides

(2021). Dynamic inference in probabilistic graphical models. In ITCS 2021.

PDF Kun's talk

(2020). What can be sampled locally?. Distributed Computing (DC) 33, 227–253 (conference version in PODC 2017).

PDF Yitong's slides

(2018). On local distributed sampling and counting. In PODC 2018.

PDF Yitong's slides