Dongdong She

Dongdong She is an Assistant Professor in the Department of Computer Science and Engineering at The Hong Kong University of Science and Technology (HKUST). His research focuses on the intersection of security and machine learning, applying data-driven approaches to solve security problems. He has established himself as a prominent researcher in software security and fuzzing techniques with publications in top conferences including IEEE S&P, CCS, and USENIX Security.

Dr. She received his Ph.D. from Columbia University's Department of Computer Science, where he worked with Professors Suman Jana and Baishakhi Ray. Prior to Columbia, he conducted research with Zhiyun Qian on Android Security at the University of California, Riverside. He completed his undergraduate studies at Huazhong University of Science and Technology.

His research spans two main areas: LLM Security, which investigates the security of large language models and LLM-powered systems, and LLM for Traditional Security, which leverages LLMs to solve traditional security problems such as program analysis and vulnerability discovery. His work often combines machine learning techniques with traditional security approaches to develop innovative solutions for software security challenges.

Dr. She's publication record shows a consistent evolution from foundational work in neural network-assisted fuzzing (NEUZZ) toward more advanced applications in LLM security and program analysis, demonstrating both theoretical rigor and practical impact with techniques adopted by the security community.

Among his notable achievements:

• Distinguished Paper Award at ISSTA 2025
• Distinguished Paper Award at IEEE S&P 2025
• Best Paper Award Runner-Up at CCS 2022
• Second Place in SBFT 2024 Fuzzing Competition
• Finalist in 2019 NYU CSAW Applied Research Competition

Dr. She currently advises several Ph.D. students including Yuchong Xie, Shuangjie Yao, and Qiao Zhang, who began their studies in Fall 2024. He serves on program committees for major conferences including ASE 2025, where he is a PC Member for the Research Papers track. His research is supported by grants enabling his team to pursue innovative approaches at the intersection of machine learning and security.

His research group maintains active collaborations with institutions worldwide and contributes to open-source security tools that are widely used in both academia and industry, with a particular focus on developing advanced techniques for software security analysis through the application of machine learning.