Private search on key-value stores with hierarchical indexes. - Haibo Hu 001; Jianliang Xu; Xizhong Xu; Kexin Pei; Byron Choi; Shuigeng Zhou (ICDE, 2014)

LEAPS: Detecting Camouflaged Attacks with Statistical Learning Guided by Program Analysis. - Zhongshu Gu; Kexin Pei; Qifan Wang 001; Luo Si; Xiangyu Zhang 001; Dongyan Xu (DSN, 2015)

HERCULE: attack story reconstruction via community discovery on correlated log graph. - Kexin Pei; Zhongshu Gu; Brendan Saltaformaggio; Shiqing Ma; Fei Wang 001; Zhiwei Zhang; Luo Si; Xiangyu Zhang 001; Dongyan Xu (ACSAC, 2016)

Python probabilistic type inference with natural language support. - Zhaogui Xu; Xiangyu Zhang 001; Lin Chen 015; Kexin Pei; Baowen Xu (SIGSOFT FSE, 2016)

HVLearn: Automated Black-Box Analysis of Hostname Verification in SSL/TLS Implementations. - Suphannee Sivakorn; George Argyros; Kexin Pei; Angelos D. Keromytis; Suman Jana (IEEE Symposium on Security and Privacy, 2017)

Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems. - Kexin Pei; Yinzhi Cao; Junfeng Yang; Suman Jana (CoRR, 2017)

DeepTest: automated testing of deep-neural-network-driven autonomous cars. - Yuchi Tian; Kexin Pei; Suman Jana; Baishakhi Ray (ICSE, 2018)

Efficient Formal Safety Analysis of Neural Networks. - Shiqi Wang 002; Kexin Pei; Justin Whitehouse; Junfeng Yang; Suman Jana (NeurIPS, 2018)

Formal Security Analysis of Neural Networks using Symbolic Intervals. - Shiqi Wang 002; Kexin Pei; Justin Whitehouse; Junfeng Yang; Suman Jana (USENIX Security Symposium, 2018)

NEUZZ: Efficient Fuzzing with Neural Program Learning. - Dongdong She; Kexin Pei; Dave Epstein; Junfeng Yang; Baishakhi Ray; Suman Jana (CoRR, 2018)

NEUZZ: Efficient Fuzzing with Neural Program Smoothing. - Dongdong She; Kexin Pei; Dave Epstein; Junfeng Yang; Baishakhi Ray; Suman Jana (IEEE Symposium on Security and Privacy, 2019)

DeepXplore: automated whitebox testing of deep learning systems. - Kexin Pei; Yinzhi Cao; Junfeng Yang; Suman Jana (Commun. ACM, 2019)

Bringing Engineering Rigor to Deep Learning. - Kexin Pei; Shiqi Wang 002; Yuchi Tian; Justin Whitehouse; Carl Vondrick; Yinzhi Cao; Baishakhi Ray; Suman Jana; Junfeng Yang (ACM SIGOPS Oper. Syst. Rev., 2019)

Trex: Learning Execution Semantics from Micro-Traces for Binary Similarity. - Kexin Pei; Zhou Xuan; Junfeng Yang; Suman Jana; Baishakhi Ray (CoRR, 2020)

XDA: Accurate, Robust Disassembly with Transfer Learning. - Kexin Pei; Jonas Guan; David Williams-King; Junfeng Yang; Suman Jana (NDSS, 2021)

StateFormer: fine-grained type recovery from binaries using generative state modeling. - Kexin Pei; Jonas Guan; Matthew Broughton; Zhongtian Chen; Songchen Yao; David Williams-King; Vikas Ummadisetty; Junfeng Yang; Baishakhi Ray; Suman Jana (ESEC/SIGSOFT FSE, 2021)

SymLM: Predicting Function Names in Stripped Binaries via Context-Sensitive Execution-Aware Code Embeddings. - Xin Jin; Kexin Pei; Jun Yeon Won; Zhiqiang Lin 001 (CCS, 2022)

NeuDep: neural binary memory dependence analysis. - Kexin Pei; Dongdong She; Michael Wang; Scott Geng; Zhou Xuan; Yaniv David; Junfeng Yang; Suman Jana; Baishakhi Ray (ESEC/SIGSOFT FSE, 2022)

Development of a Neural Network-Based Mathematical Operation Protocol for Embedded Hexadecimal Digits Using Neural Architecture Search (NAS). - Victor Robila; Kexin Pei; Junfeng Yang (CoRR, 2022)

Can Large Language Models Reason about Program Invariants? - Kexin Pei; David Bieber; Kensen Shi; Charles Sutton; Pengcheng Yin (ICML, 2023)

TraceFixer: Execution Trace-Driven Program Repair. - Islem Bouzenia; Yangruibo Ding; Kexin Pei; Baishakhi Ray; Michael Pradel (CoRR, 2023)

Symmetry-Preserving Program Representations for Learning Code Semantics. - Kexin Pei; Weichen Li; Qirui Jin; Shuyang Liu; Scott Geng; Lorenzo Cavallaro; Junfeng Yang; Suman Jana (CoRR, 2023)

Analyzing and Securing Software via Robust and Generalizable Learning - Kexin Pei (2023)

Learning Approximate Execution Semantics From Traces for Binary Function Similarity. - Kexin Pei; Zhou Xuan; Junfeng Yang; Suman Jana; Baishakhi Ray (IEEE Trans. Software Eng., 2023)

SWE-bench: Can Language Models Resolve Real-world Github Issues? - Carlos E. Jimenez; John Yang 002; Alexander Wettig; Shunyu Yao; Kexin Pei; Ofir Press; Karthik R. Narasimhan (ICLR, 2024)

Exploiting Code Symmetries for Learning Program Semantics. - Kexin Pei; Weichen Li; Qirui Jin; Shuyang Liu; Scott Geng; Lorenzo Cavallaro; Junfeng Yang; Suman Jana (ICML, 2024)

TRACED: Execution-aware Pre-training for Source Code. - Yangruibo Ding; Benjamin Steenhoek; Kexin Pei; Gail E. Kaiser; Wei Le; Baishakhi Ray (ICSE, 2024)

SmartInv: Multimodal Learning for Smart Contract Invariant Inference. - Sally Junsong Wang; Kexin Pei; Junfeng Yang (SP, 2024)

Comment on Revisiting Neural Program Smoothing for Fuzzing. - Dongdong She; Kexin Pei; Junfeng Yang; Baishakhi Ray; Suman Jana (CoRR, 2024)

Detecting Buggy Contracts via Smart Testing. - Sally Junsong Wang; Jianan Yao; Kexin Pei; Hidedaki Takahashi; Junfeng Yang (CoRR, 2024)

The Hitchhiker's Guide to Program Analysis, Part II: Deep Thoughts by LLMs. - Haonan Li; Hang Zhang 012; Kexin Pei; Zhiyun Qian (CoRR, 2025)

EditLord: Learning Code Transformation Rules for Code Editing. - Weichen Li; Albert Jan; Baishakhi Ray; Chengzhi Mao; Junfeng Yang; Kexin Pei (CoRR, 2025)

Synthesizing Performance Constraints for Evaluating and Improving Code Efficiency. - Jun Yang; Cheng-Chi Wang; Bogdan Alexandru Stoica; Kexin Pei (CoRR, 2025)