Lingming Zhang

Lingming Zhang is an Associate Professor in the Department of Computer Science at the Grainger College of Engineering, University of Illinois Urbana-Champaign. He maintains an active research program focused on the intersection of Software Engineering, Programming Languages, and Machine Learning, with particular emphasis on LLM-based software testing, repair, and synthesis.

His research has resulted in over 100 publications with an h-index exceeding 50. His work has practical impact, having helped detect over 1,000 bugs and vulnerabilities in open-source projects from Apache and GitHub, as well as software systems from major technology companies including eBay, Google, Meta/Facebook, Microsoft, NVIDIA, OctoML, Oracle, and Yahoo!.

Zhang's research interests span several key areas in software engineering and programming languages:

• LLM-based software testing, debugging, and repair
• Code generation and understanding with large language models
• Automated program repair
• Software testing and fuzzing techniques
• Deep learning for software engineering

His recent publications demonstrate a strong focus on leveraging large language models for software engineering tasks, with notable contributions including Agentless, TitanFuzz, AlphaRepair, and ChatRepair. His group has also released influential open-source code language models including StarCoder2 and Magicoder, which have garnered over 1 million downloads worldwide.

Zhang has received numerous prestigious awards and recognitions for his work:

• ACM Distinguished Member
• ACM SIGSOFT Early Career Researcher Award
• NSF CAREER Award
• UIUC Dean's Award for Excellence in Research
• Multiple ACM SIGSOFT Distinguished Paper Awards

He has successfully mentored numerous PhD and MS students, many of whom have gone on to positions at top technology companies and academic institutions. His research is supported by grants from major technology companies including Alibaba, Amazon, Google, Kwai Inc, Meta/Facebook, NVIDIA, and Samsung. Currently, he serves as program co-chair for ASE 2025 and LLM4Code 2025, demonstrating his leadership in the software engineering research community.