I am a Ph.D. candidate in Computer Science and Engineering from the University at Buffalo, advised by Dr. Haipeng Cai. Before that, I was a Ph.D. candidate in the School of Electrical Engineering and Computer Science at Washington State University.

My research lies at the intersection of Software Engineering, Software Security, and Artificial Intelligence. I develop data-centric and AI-assisted techniques for software vulnerability detection, classification, repair, benchmarking, and security dataset construction.

News

• 10/2025: Our paper Exploring and Improving Real-World Vulnerability Data Generation via Prompting Large Language Models was accepted to ICSE 2026.
• 08/2025: I presented our paper APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching at USENIX Security 2025 in Seattle, WA.
• 08/2025: I received a student travel grant from USENIX Security 2025.
• 04/2025: I received a travel grant from IEEE S&P 2025.
• 03/2025: Our paper Code Speaks Louder: Exploring Security and Privacy Relevant Regional Variations in Mobile Applications was accepted to IEEE S&P 2025.
• 01/2025: Our paper APPATCH: Automated Adaptive Prompting Large Language Models for Real-World Software Vulnerability Patching was accepted to USENIX Security 2025.
• 04/2024: I received the Best RA Award from the School of Electrical Engineering and Computer Science at Washington State University.
• 10/2023: Our paper VGX: Large-Scale Sample Generation for Boosting Learning-Based Software Vulnerability Analyses was accepted to ICSE 2024.
• 12/2022: Our paper VulGen: Realistic Vulnerable Sample Generation via Pattern Mining and Deep Learning was accepted to ICSE 2023.
• 09/2022: Our paper Open Science in Software Engineering: A Study on Deep Learning-Based Vulnerability Detection was accepted to IEEE TSE.
• 06/2022: Our paper Generating Realistic Vulnerabilities via Neural Code Editing: An Empirical Study was accepted to FSE 2022.