People

Di Wang is an Assistant Professor of the School of Computer Science, Peking University. His research focuses are programming languages, formal verification, and probabilistic programming; his broader interests include type theory, program synthesis, concurrency, and Bayesian inference. His current main research directions include: (1) the mathematical foundations and domain-specific toolchains of general-purpose probabilistic programming, (2) the design and implementation of resource-safe programming languages, (3) quantitative analysis and verification of randomness and uncertainty in software systems.

Education

2017-2022, Ph.D., Carnegie Mellon University

2013-2017, Bachelor of Science, Peking University

Selected Publications

1. Di Wang, Jan Hoffmann, and Thomas Reps. Sound Probabilistic Inference via Guide Types. PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation. June 2021.

2. Di Wang, Jan Hoffmann, and Thomas Reps. Central Moment Analysis for Cost Accumulators in Probabilistic Programs. PLDI 2021: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation. June 2021.

3. Di Wang, David M. Kahn, and Jan Hoffmann. Raising Expectations: Automating Expected Cost Analysis with Types. Proceedings of the ACM on Programming Languages, Volume 4, Issue ICFP. August 2020.

4. Tristan Knoth, Di Wang, Nadia Polikarpova, and Jan Hoffmann. Resource-Guided Program Synthesis. PLDI 2019: Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation. June 2019.

5. Di Wang and Jan Hoffmann. Type-Guided Worst-Case Input Generation. Proceedings of the ACM on Programming Languages, Volume 3, Issue POPL. January 2019.

6. Di Wang, Jan Hoffmann, and Thomas Reps. PMAF: An Algebraic Framework for Static Analysis of Probabilistic Programs. PLDI 2018: Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation. June 2018.