Martin Rinard is a Professor in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology and a member of the Computer Science and Artificial Intelligence Laboratory. His research focuses on software systems and related topics, including computer security, program analysis and compilation, machine learning and programming, approximate computing, and software robustness and reliability.

Rinard’s research results have consistently placed him at the forefront of his chose field for decades. Rinard’s program repair research produced automatic techniques that enable software systems to execute successfully through otherwise fatal errors and security vulnerabilities, recover from data corruptions, and generate patches that eliminate defects in both running systems and software code bases, all of which enable software systems to productively survive software defects and errors. His approximate computing research automatically discovers latent tradeoffs between performance and accuracy by identifying subcomputations that can be discarded or modified to deliver large performance increases with small accuracy decreases. More recently, Rinard, with his graduate student Charles Jin, found that large language models (LLMs) may develop their own understanding of reality as a way to improve their generative abilities.

Professor Rinard holds a PhD in Computer Science from Stanford University. He is an ACM Fellow and has received many awards including an Alfred P. Sloan Research Fellowship, an NSF CAREER Award, 2025 ACM Special Interest Group on Software Engineering (SIGSOFT), and Distinguished and Best Paper Awards from numerous publication venues.