Kexin Pei

Kexin Pei is an Assistant Professor in the Department of Computer Science at the University of Chicago. His research focuses on the intersection of Security, Software Engineering, and Machine Learning, emphasizing data-driven program analysis techniques to enhance software reliability and security. He holds a Ph.D. from Columbia University's Computer Science department.

• Affiliations: University of Chicago, Google DeepMind (Research Collaboration), Microsoft Research (Past Internship)
• Education: Ph.D., Computer Science, Columbia University

Research interests include Machine Learning for Code, Program Analysis, Software Security, AI & Machine Learning Foundations, and Systems Architecture. His work has received awards such as the Neubauer Family Assistant Professorship (2024), Best Paper Award at SOSP (2017), and Distinguished Artifact Award (2017).

Recent publications explore topics like neural program analysis, binary similarity, and cybersecurity evaluation through Capture-the-Flag challenges. He collaborates with industry partners like Google DeepMind to develop program analysis tools using large language models.

• Awards:
  • Neubauer Family Assistant Professorship (2024)
  • Google Conference Scholarship (2023)
  • CSAW Applied Research Competition Runner-Up (2018)
• Grants/Projects: Leads research on adversarial testing for deep learning systems and neural program smoothing for fuzzing.

Active in academic service, he serves on program committees for top venues like ICSE, CCS, and USENIX Security.