Kexin Pei

Neubauer Family Assistant Professor

Department of Computer Science

The University of Chicago

Contact: kpei@uchicago.edu

Office: JCL 260

Google Scholar, X, LinkedIn

Prospective students: I am always looking for brilliant students. I am especially interested in working with self-motivated students who (1) have experience with Code Reasoning and Program Analysis, and (2) have a solid background in Machine Learning. Drop me an email if you are interested in working with me.

About Me

I am a Neubauer Family Assistant Professor at the Department of Computer Science, The University of Chicago. I received my PhD at Department of Computer Science, Columbia University (advised by Suman Jana, Junfeng Yang, and working closely with Baishakhi Ray). I obtained my MS at Department of Computer Science, Purdue University (advised by Dongyan Xu, Xiangyu Zhang, and Luo Si) and BS in Department of Computer Science, HKBU (advised by Haibo Hu and Jianliang Xu). 

I am broadly interested in Security, Software Engineering, and Machine Learning, with a focus on automated code reasoning to improve the security and reliability of software systems. I get most excited about developing intelligent systems that understand code semantics and can precisely reason about program behaviors, allowing them to reliably and efficiently analyze, detect, and fix software bugs and vulnerabilities.

Publication

1. Weichen Li, Jiamin Song, Bogdan Alexandru Stoica, Arav Dhoot, Gabriel Ryan, Shengyu Fu, Kexin Pei. "SemRep: Generative Code Representation Learning with Code Transformations'', in the Forty-Third International Conference on Machine Learning (ICML 2026). [pdf]

2. Jun Yang, Yuechun Sun, Yi Wu, Rodrigo Caridad, Yongwei Yuan, Jianan Yao, Shan Lu, Kexin Pei. "ExVerus: Verus Proof Repair via Counterexamples Reasoning", in the Forty-Third International Conference on Machine Learning (ICML 2026). [pdf]

3. Michael Wang, Kexin Pei, Armando Solar-Lezama. "ABSINT-AI: Language Models for Abstract Interpretation", in the Forty-Third International Conference on Machine Learning (ICML 2026). [pdf]

4. Yifan Zhang, Jieyu Li, Kexin Pei, Yu Huang, Kevin Leach. "SynthFix: Adaptive Neuro-Symbolic Code Vulnerability Repair", in the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026). [pdf]

5. Zirui Zhang, Haoyu Dong, Kexin Pei, Chengzhi Mao. "C3R: Cross-Modal Cycle Consistency Rewards Improve Multimodal Reasoning", in the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026 (CVPR 2026). [pdf]

6. Jie Zhu, Chihao Shen, Ziyang Li, Jiahao Yu, Yizheng Chen, Kexin Pei. "Locus: Agentic Predicate Synthesis for Directed Fuzzing", in Forty-Eighth IEEE/ACM International Conference on Software Engineering (ICSE 2026). [pdf][code]

7. Jun Yang, Cheng-Chi Wang, Bogdan Alexandru Stoica, Kexin Pei. "Synthesizing Performance Constraints for Evaluating and Improving Code Efficiency", in Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025). [pdf][code][data]

8. Haonan Li, Hang Zhang, Kexin Pei, Zhiyun Qian. "Towards More Accurate Static Analysis for Taint-style Bug Detection in Linux Kernel", in IEEE/ACM International Conference on Automated Software Engineering (ASE 2025). [pdf]

9. Michael Wang, Kexin Pei, Armando Solar-Lezama. "CALLME: Call Graph Augmentation with Large Language Models for Javascript", in Proceedings of the Second Conference on Language Modeling (COLM 2025). [pdf]

10. Weichen Li, Albert Jan, Baishakhi Ray, Chengzhi Mao, Junfeng Yang, Kexin Pei. "EditLord: Learning Code Transformation Rules for Code Editing" in 42nd International Conference on Machine Learning (ICML 2025). [pdf][code]

11. Kexin Pei, Weichen Li, Qirui Jin, Shuyang Liu, Scott Geng, Lorenzo Cavallaro, Junfeng Yang, Suman Jana. "Exploiting Code Symmetries for Learning Program Semantics" (Spotlight Top-3.5%) in 41st International Conference on Machine Learning (ICML 2024). [pdf][code]

12. Sally Junsong Wang, Kexin Pei, Junfeng Yang. "SMARTINV: Multimodal Learning for Smart Contract Invariant Inference" in 45th IEEE Symposium on Security and Privacy (Oakland S&P 2024). [pdf][code]

13. Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, Karthik Narasimhan. "SWE-bench: Can Language Models Resolve Real-World GitHub Issues?" (Oral Top-1.2%) in 12th International Conference on Learning Representations (ICLR 2024). [pdf][code][website]

14. Yangruibo Ding, Ben Steenhoek, Kexin Pei, Gail Kaiser, Wei Le, Baishakhi Ray. "TRACED: Execution-aware Pre-training for Source Code" in Proceedings of the 46th International Conference on Software Engineering (ICSE 2024). [pdf]

15. John Yang, Akshara Prabhakar, Shunyu Yao, Kexin Pei, Karthik R Narasimhan. "Language Agents as Hackers: Evaluating Cybersecurity Skills with Capture the Flag" (Best Paper Award, Oral) in Multi-Agent Security, Co-located with the 37th Conference on Neural Information Processing Systems (MASEC@NeurIPS 2023). [pdf]

16. Kexin Pei, David Bieber, Kensen Shi, Charles Sutton, Pengcheng Yin. "Can Large Language Models Reason about Program Invariants?" in Proceedings of the 40th International Conference on Machine Learning (ICML 2023). [pdf][poster]

17. Kexin Pei, Dongdong She*, Michael Wang*, Scott Geng*, Zhou Xuan, Yaniv David, Junfeng Yang, Suman Jana, Baishakhi Ray. "NeuDep: Neural Binary Memory Dependence Analysis", in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022). [pdf][code]

18. Xin Jin, Kexin Pei, Jun Yeon Won, Zhiqiang Lin. "SymLM: Predicting Function Names in Stripped Binaries via Context-Sensitive Execution-Aware Code Embeddings", in ACM Conference on Computer and Communications Security (CCS 2022). [pdf][code][poster]

19. Kexin Pei, Zhou Xuan, Junfeng Yang, Suman Jana, Baishakhi Ray. "Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity", in IEEE Transactions on Software Engineering (TSE 2022). [pdf][code][ghidra plugin][short video][slides]

20. Kexin Pei, Jonas Guan, Matthew Broughton, Zhongtian Chen, Songchen Yao, David Williams-King, Vikas Ummadisetty, Junfeng Yang, Baishakhi Ray, Suman Jana. "StateFormer: Fine-Grained Type Recovery from Binaries using Generative State Modeling", in Proceedings of the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2021). [pdf][code][slides][video]

21. Kexin Pei*, Jonas Guan*, David Williams-King, Junfeng Yang, Suman Jana. "XDA: Accurate, Robust Disassembly with Transfer Learning", in Proceedings of the 2021 Network and Distributed System Security Symposium (NDSS 2021). [pdf][code][slides][video]

22. Dongdong She, Kexin Pei, Dave Epstein, Junfeng Yang, Baishakhi Ray, Suman Jana. "NEUZZ: Efficient Fuzzing with Neural Program Smoothing", in Proceedings of the 40th IEEE Symposium on Security and Privacy (Oakland S&P 2019). [pdf][code][slides][video]

23. Kexin Pei, Linjie Zhu, Yinzhi Cao, Junfeng Yang, Carl Vondrick, Suman Jana. "Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems". in ICSE 2019 Workshop on Testing for Deep Learning and Deep Learning for Testing (DeepTest 2019). [pdf]

24. Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana. "Efficient Formal Safety Analysis of Neural Networks", in Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS 2018). [pdf][code][video]

25. Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana. "Formal Security Analysis of Neural Networks using Symbolic Intervals", in Proceedings of the 27th USENIX Security Symposium (USENIX Security 2018). [pdf][code][video]

26. Yuchi Tian, Kexin Pei, Suman Jana, Baishakhi Ray. "DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars", in Proceedings of the 40th International Conference on Software Engineering (ICSE 2018). [pdf][code][results]

27. Kexin Pei, Yinzhi Cao, Junfeng Yang, Suman Jana. "DeepXplore: Automated Whitebox Testing of Deep Learning Systems" (Best Paper Award, Runner-Up in CSAW 2018 Top-10 Finalist of Applied Research Competition, ACM SigMobile Research Highlight, MLSec@NIPS'17), in Proceedings of the 26th ACM Symposium on Operating Systems Principles (SOSP 2017). [pdf][code][poster][slides][CACM research highlight][video].

28. Suphannee Sivakorn, George Argyros, Kexin Pei, Angelos D. Keromytis, Suman Jana. "HVLearn: Automated Black-box Analysis of Hostname Verification in SSL/TLS Implementations", in Proceedings of the 38th IEEE Symposium on Security and Privacy (Oakland S&P 2017). [pdf][code]

29. Kexin Pei, Zhongshu Gu, Brendan Saltaformaggio, Shiqing Ma, Fei Wang, Zhiwei Zhang, Luo Si, Xiangyu Zhang, Dongyan Xu. "HERCULE: Attack Story Reconstruction via Community Discovery on Correlated Log Graph", in Proceedings of the 32nd Annual Computer Security Applications Conference (ACSAC 2016). [pdf]

30. Zhaogui Xu, Xiangyu Zhang, Lin Chen, Kexin Pei, Baowen Xu. "Python Probabilistic Type Inference with Natural Language Support" (Distinguished Artifact Award), in Proceedings of the 24th ACM SIGSOFT Symposium on the Foundations of Software Engineering (FSE 2016). [pdf] 

31. Zhongshu Gu, Kexin Pei, Qifan Wang, Luo Si, Xiangyu Zhang, Dongyan Xu. "LEAPS: Detecting Camouflaged Attacks with Statistical Learning Guided by Program Analysis", in Proceedings of the 45th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2015). [pdf]

32. Haibo Hu, Jianliang Xu, Xizhong Xu, Kexin Pei, Byron Choi, Shuigeng Zhou. "Private Search on Key-Value Stores with Hierarchical Indexes", in Proceedings of the 30th IEEE International Conference on Data Engineering (ICDE 2014). [pdf]

Teaching

• CMSC 220: Software Construction, Spring 2025

• CMSC 32002: Topics in Software Engineering: Machine Learning for Programming, Winter 2025, Winter 2026

• CMSC 331: Advanced Operating Systems, Fall 2024, Winter 2026

Group

• PhD: Jie Zhu, Weichen Li, Jun Yang (with Shan Lu)

• MS: Sam Huang, Yiming Cheng (with Junchen Jiang), Yi Wu (with Chenhao Tan)

• BS: Laurel Li (UChicago Quad Undergraduate Research Scholar, T. D. Lee Fellowship), Rodrigo Caridad, Chris Low (UChicago Quad Undergraduate Research Scholar), Jack Luo

• Student collaborators: Rani Ayu (UChicago), Bogdan Alexandru Stoica (UChicago), Michael Wang (MIT), Qirui Jin (Georgia Tech), Ruidan Li (UChicago)

• Visiting students: Jiahao Yu (Northwestern University), Chihao Steven Shen (UMD), Cheng-Chi Casper Wang (National Taiwan University), Jiamin Song, Yuechun Sun

Work

• Research Intern @ Google DeepMind, Mountain View, CA (2022), working with Charles Sutton, David Bieber, Kensen Shi, Pengcheng Yin, and Henryk Michalewski.

• Research Intern @ RiSE, Microsoft Research, Redmond, WA (2018), working with Madan Musuvathi, Todd Mytkowicz, and Saeed Maleki. 

Services

• PC Co-Chair. 3rd International Workshop on Large Language Models for Code (LLM4Code 2026), Co-located with IEEE/ACM International Conference on Software Engineering (ICSE 2026)

• PC Co-Chair. 8th Deep Learning Security and Privacy Workshop (DLSP 2025), Co-located with IEEE Security & Privacy (S&P 2025)

• Program Committee. 

  • SOSP 2026

  • ICSE 2026, 2027

  • CCS 2025, 2026

  • IEEE S&P 2024, 2026

  • USENIX Security 2025, 2026

  • USENIX ATC 2024

  • ASE 2024

  • ISSTA 2024

Media Coverage

• Interview with CACM, Scientific American, IEEE Spectrum, Newsweek, TechRadar, Columbia News, Sohu, Sina, CCTV Hello, AI Documentary