I am currently a third-year Ph.D. student in the School of Electrical and Computer Engineering at Purdue University, advised by Prof. Xipeng Shen. I also collaborate closely with Oak Ridge National Laboratory, under the supervision of Dr. M.A.H. Monil and Dr. Seyong Lee. I received my Bachelor’s degree in Computer Science and Engineering from Huazhong University of Science and Technology in 2023. During my undergraduate studies, I was an exchange student at the University of California, Santa Barbara (2022–2023), supervised by Prof. Zheng Zhang.
🎓 Research Interests
My research lies at the intersection of compilers and AI systems. I develop program analysis, compilation, and runtime techniques to make AI systems efficient and reliable, and use AI to improve how programs are analyzed and optimized.
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AI for Programming Systems: Combining LLMs with program analysis and verification feedback to improve code generation, program translation, and performance optimization.
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Compilers and Systems for AI: Developing program representations and compiler optimizations for agentic systems, and execution mechanisms for AI workloads on heterogeneous hardware. I am also interested in extending these ideas to embodied AI, where inference and action must operate under resource and timing constraints.
🔥 News
- 👨🏻💻 2026.04: I will join Qualcomm GPU teams as an intern this summer at Santa Clara, CA! I will work on compiler optimization framework for Qualcomm GPUs.
- 🎓 2026.03: I will transfer to Purdue University along with My advisor Prof.Shen!
- 🎉 2026.01: Our paper "SparseX: Synergizing GPU Libraries for Sparse Matrix Multiplication on Heterogeneous Processors" has been accepted by CGO 2026!
- 🎉 2025.04: Our paper "Reductive Analysis with Compiler-Guided Large Language Models for Input-Centric Code Optimizations" has been accepted by PLDI 2025! See you in Seoul in June!
- 🎉 2025.01: I'm glad to share that I've passed the Ph.D. written exam and have become a Ph.D. candidate!
📝 Publications
Reductive Analysis with Compiler-Guided Large Language Models for Input-Centric Code Optimizations
Xiangwei Wang, Xinning Hui, Chunhua Liao, Xipeng Shen
ACM SIGPLAN Conference on Programming Language Design & Implementation (PLDI), 2025
Accept Rate (28.4% ≈ 90/316)
SparseX: Synergizing GPU Libraries for Sparse Matrix Multiplication on Heterogeneous Processors
Ruifeng Zhang, Xiangwei Wang, Ang Li, Xipeng Shen
2026 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)
HardGBM: A Framework for Accurate and Hardware-Efficient Gradient Boosting Machines
Hongfei Wang, Zhanfei Wu, Xiangwei Wang, Longyun Bian, Hai Jin
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2023
Ultra Memory-Efficient On-FPGA Training of Transformers via Tensor-Compressed Optimization
Jiayi Tian, Jinming Lu, Hai Li, Xiangwei Wang, Cong (Callie)Hao, Ian Young, Zheng Zhang
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2025
🎖 Honors and Awards
- University-Level Merit Student Award, 2020/2021/2022
- Outstanding Undergraduate Graduates, 2023
- Outstanding Undergraduate Thesis, 2023
📖 Educations
2023.09 - now, Purdue University, Ph.D. student in Electrical and Computer Engineering;
2022.09 - 2023.06, University of California, Santa Barbara (UCSB), Exchange student in Computer Science;
2019.09 - 2023.06, Huazhong University of Science and Technology (HUST), Bachelor of Engineering in Cyberspace Security (Top 1%)
💻 Internships
- 2024.05 - 2024.08, Oak Ridge National Laboratory.
- 2025.05 - 2025.08, Oak Ridge National Laboratory.
- 2026.05 - 2026.08, Qualcomm GPU Compiler Team.