Kexin is an incoming Assistant Professor, starting July of 2024.

His research lies at the intersection of Security, Software Engineering, and Machine Learning, focusing on developing data-driven program analysis approaches to improve the security and reliability of traditional and AI-based software systems. He gets most excited about developing machine learning models that can reason about program structure and behavior to precisely and efficiently analyze, detect, and fix software bugs and vulnerabilities.

His research has received the Best Paper Award in SOSP, a Distinguished Artifact Award, been featured in CACM Research Highlight, and won CSAW Applied Research Competition Runner-Up. He works with Learning for Code team at Google DeepMind, building program analysis tools based on large language models. Kexin received his Ph.D. in Computer Science from Columbia University.

Focus Areas: Machine Learning for Code, Program Analysis, Software Security and Reliability


AI & Machine Learning

Foundations and applications of computer algorithms making data-centric models, predictions, and decisions

Security & Privacy

Understanding and defending against emerging threats in our increasingly computational world

Systems, Architecture & Networking

Design and analysis of computing systems: cloud, edge, Internet, quantum, and beyond
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