Weiming Feng

Weiming Feng

fwm1994 at gmail dot com

Nanjing University

Biography

I am Weiming Feng (凤维明), a Ph.D. student in the theory group in the Department of Computer Science and Technology at Nanjing University. My advisor is Professor Yitong Yin. Before I studied in Nanjing University, I obtained B.Eng. degree from University of Electronic Science and Technology of China in June 2016, where I majored in Network Engineering.

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.

Interests

  • Sampling/counting algorithms
  • Markov chain theory
  • Distributed algorithms

Education

  • Ph.D. in Computer Science, 2016 - present

    Nanjing University (NJU)

  • B.Eng. in Network Engineering, 2012 - 2016

    University of Electronic Science and Technology of China (UESTC)

Publications

Fast sampling and counting $k$-SAT solutions in the local lemma regime. In Proceedings of the 52nd ACM Symposium on Theory of Computing (STOC), 2020.

PDF Slides Video

Dynamic sampling from graphical models. In Proceedings of the 51st ACM Symposium on Theory of Computing (STOC), 2019.

PDF Poster Slides

On local distributed sampling and counting. In Proceedings of the 37th ACM Symposium on Principles of Distributed Computing (PODC), 2018.

PDF

What can be sampled locally?. In Proceedings of the 36th ACM Symposium on Principles of Distributed Computing (PODC), 2017.

PDF

Teaching Assistant

Advanced Algorithms

graduate course, Nanjing University

Advanced Algorithms

graduate course, Nanjing University

Awards

National Scholarship

ACM-ICPC Asia Regional Contest, Gold Medals