# 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

Rapid mixing from spectral independence beyond the Boolean domain. To Appear in Proceedings of the 32nd ACM-SIAM Symposium on Discrete Algorithms (SODA), 2021.

Distributed Metropolis sampler with optimal parallelism. To Appear in Proceedings of the 32nd ACM-SIAM Symposium on Discrete Algorithms (SODA), 2021.

Dynamic inference in probabilistic graphical models. To Appear in Proceedings of the 12th Innovations in Theoretical Computer Science (ITCS), 2021.

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.

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

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

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