Department of Computer Science and the College
Bo Li’s research addresses trustworthy machine learning from both theoretical and practical aspects, and aims to enable reliable machine learning algorithms and systems in the real world, such as safe autonomous vehicles, federated (distributed) learning, and trustworthy large language models. She focuses on different trustworthiness perspectives, such as robustness, privacy, and generalization, as well as their underlying connections. She has designed several scalable frameworks for robust learning, privacy-preserving data publishing systems, and trustworthiness evaluation for large language models.
Li is the recipient of an Alfred P. Sloan Research Fellowship, a National Science Foundation CAREER award, the International Joint Conferences on Artificial Intelligence Computers and Thought Award, the MIT Technology Review TR-35 Award, an Intel Rising Star Faculty Award, a Symantec Research Labs Fellowship, and research awards from Amazon, Meta, Google, Intel, MSR, eBay, and IBM. She was also recognized with best paper awards at multiple top machine learning and security conferences. Her research has been featured by such major publications and media outlets as Nature, Wired, Fortune, and The New York Times.
Li holds a PhD in computer science from Vanderbilt University and was a postdoctoral researcher at the University of California, Berkeley. Most recently, she was an assistant professor of computer science at the University of Illinois Urbana-Champaign, where she was honored with its C. W. Gear Outstanding Junior Faculty Award.