Zhe Zhou

LWSN 3133 (#55) · Purdue University, West Lafayette, IN 47907 · zhou956@purdue.edu

I am currently a PhD candidate in Computer Science at Purdue University, advised by Prof. Suresh Jagannathan, and I work closely with Prof. Benjamin Delaware. Prior to starting my PhD program, I was a full-time software engineer at Magvii (Face++) from 2017 to 2018. I received my bachelor's degree from Peking University in 2017, advised by Prof. Guangyu Sun.

My research interests lie in programming languages (PL), particularly in (automated) program verification and synthesis, property-based testing, and refinement types. My work focuses on two main streams: one involves verifying coverage completeness and incorrectness using underapproximation-style reasoning, and the other involves verifying effectful programs and distributed system using a trace-based approach and automata techniques. I am also interested in the intersection of machine learning (ML) and PL -- leveraging ML to solve PL problems, and vice versa.

Here is my CV.

Publications

Derivative-Guided Symbolic Execution

Yongwei Yuan, Zhe Zhou, Julia Belyakova, and Suresh Jagannathan

Proceedings of the ACM on Programming Languages (POPL 2025)

(doi) (full version) (artifact)

A HAT Trick: Automatically Verifying Representation Invariants Using Symbolic Finite Automata

Zhe Zhou, Qianchuan Ye, Benjamin Delaware, and Suresh Jagannathan

ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2024)

(doi) (draft) (full version) (artifact)

Covering All the Bases: Type-Based Verification of Test Input Generators

Zhe Zhou, Ashish Mishra, Benjamin Delaware, and Suresh Jagannathan

ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI 2023)

(Distinguished Paper Award) (doi) (draft) (full version) (artifact)

Data-Driven Abductive Inference of Library Specifications

Zhe Zhou, Robert Dickerson, Benjamin Delaware, and Suresh Jagannathan

ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA 2021)

(Distinguished Artifact Award) (doi) (full version) (artifact) (talk)