About me

I am a PhD student at the Department of Computer Science at ETH Zurich from fall 2018 to spring 2024 (expected). I am part of the Secure, Reliable, and Intelligent Systems Lab and am supervised by Prof. Martin Vechev. My research focuses on exploring the synergy of machine learning and programming, as well as its implication for security and reliability.



Instruction Tuning for Secure Code Generation
Jingxuan He*, Mark Vero*, Gabriela Krasnopolska, Martin Vechev
ICML 2024 * Equal contribution
Code Agents are State of the Art Software Testers
Niels Mündler, Mark Niklas Müller, Jingxuan He, Martin Vechev
arXiv 2024
Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation
Niels Mündler, Jingxuan He, Slobodan Jenko, Martin Vechev
ICLR 2024
Exploiting LLM Quantization
Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin Vechev
arXiv 2024 NextGenAISafety@ICML24 Oral


Large Language Models for Code: Security Hardening and Adversarial Testing
Jingxuan He, Martin Vechev
ACM CCS 2023 Distinguished Paper Award


On Distribution Shift in Learning-based Bug Detectors
Jingxuan He, Luca Beurer-Kellner, Martin Vechev
ICML 2022


Learning to Explore Paths for Symbolic Execution
Jingxuan He, Gishor Sivanrupan, Petar Tsankov, Martin Vechev
ACM CCS 2021
TFix: Learning to Fix Coding Errors with a Text-to-Text Transformer
Berkay Berabi, Jingxuan He, Veselin Raychev, Martin Vechev
ICML 2021
Learning to Find Naming Issues with Big Code and Small Supervision
Jingxuan He, Cheng-Chun Lee, Veselin Raychev, Martin Vechev
PLDI 2021


Learning Fast and Precise Numerical Analysis
Jingxuan He, Gagandeep Singh, Markus Püschel, Martin Vechev
PLDI 2020


Learning to Fuzz from Symbolic Execution with Application to Smart Contracts
Jingxuan He, Mislav Balunović, Nodar Ambroladze, Petar Tsankov, Martin Vechev
ACM CCS 2019


DEBIN: Predicting Debug Information in Stripped Binaries
Jingxuan He, Pesho Ivanov, Petar Tsankov, Veselin Raychev, Martin Vechev
ACM CCS 2018