主要研究方向
王迪的研究领域主要包括程序设计语言、形式化验证、概率编程，他的研究兴趣也涉及类型理论、程序合成、并发编程、贝叶斯推断等。他目前的主要研究方向为：（1）通用概率编程的数学理论和领域特定工具链，（2）资源安全的编程语言的设计与实现，（3）软件系统中随机性和不确定性的定量分析和验证。
科研/教育经历
2017-2022，博士，美国卡内基梅隆大学
2013-2017，学士，北京大学
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.
