Jiyuan Wang

I received my Bachelor’s degree in Physics from Tsinghua University, China, in 2019. I then pursued my doctoral studies at the University of California, Los Angeles, where I obtained a Ph.D. in Computer Science in 2025. My research area is software engineering and systems, with a particular focus on testing and debugging techniques for heterogeneous computing platforms, including FPGAs, GPUs, and quantum computing. My works span topics such as multi-layer compiler debugging, full-stack compiler testing, and fuzz testing for heterogeneous applications. They have been published in ASE, ICSE, ESEC/FSE, and ASPLOS. Prior to joining Tulane, I worked as an Applied Scientist at Amazon Web Services, focusing on program analysis and formal verification for cloud systems. My research has been recognized by the SIGSOFT Research Highlight.

Publications

•  J. Wang, Y. Qiu, B. Limpanukorn, H. J. Kang, Q. Zhang, and M. Kim, “DuoReduce: Bug Isolation for Multi-Layer Extensible Compilation,” in Proceedings of ESEC/FSE 2025, Trondheim, Norway.
•  B. Limpanukorn, J. Wang, H. J. Kang, Z. Zhou, and M. Kim, “Fuzzing MLIR Compilers with Custom Mutation Synthesis,” in Proceedings of ICSE 2025, Ottawa, Canada.
•  J. Wang, Q. Zhang, H. Rong, G. H. Xu, and M. Kim, “Leveraging Hardware Probes and Optimizations for Accelerating Fuzz Testing of Heterogeneous Applications,” in Proceedings of ESEC/FSE 2023, San Francisco, California, USA.
•  Q. Zhang, J. Wang, G. H. Xu, and M. Kim, “HeteroGen: Transpiling C to Heterogeneous HLS Code with Automated Test Generation and Program Repair,” in Proceedings of ASPLOS 2022.
•  J. Wang, Q. Zhang, M. Kim, and G. H. Xu, “QDiff: Differential Testing of Quantum Software Stacks,” in Proceedings of ASE 2021 (SIGSOFT Research Highlight).
•  Q. Zhang, J. Wang, and M. Kim, “HeteroFuzz: Fuzz Testing to Detect Platform Dependent Divergence for Heterogeneous Applications,” in Proceedings of ESEC/FSE 2021.
•  J. Wang, F. Ma, and Y. Jiang, “Poster: Fuzz Testing of Quantum Program,” in Proceedings of ICST 2021 (Best Poster Award).
•  Q. Zhang, J. Wang, M. A. Gulzar, R. Padhye, and M. Kim, “BigFuzz: Efficient Fuzz Testing for Data Analytics Using Framework Abstraction,” in Proceedings of ASE 2020.