主要研究方向
王迪的研究領域主要包括程序設計語言、形式化驗證、概率編程，他的研究興趣也涉及類型理論、程序合成、并發編程、貝葉斯推斷等。他目前的主要研究方向為：（1）通用概率編程的數學理論和領域特定工具鍊，（2）資源安全的編程語言的設計與實現，（3）軟件系統中随機性和不确定性的定量分析和驗證。
科研/教育經曆
2017-2022，博士，美國卡内基梅隆大學
2013-2017，學士，beat365
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.
