Contact Info

Bo’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 and federated (distributed) learning. She focuses on three interconnected aspects: robustness, privacy, generalization, and their underlying connections.

Bo received her Ph.D. in Computer Science from Vanderbilt University in 2016. She was a Postdoctoral Researcher at UC Berkeley 2017-2018 (working with Prof. Dawn Song) and joined the faculty at UIUC in 2018.

She been recognized by a long list of notable awards and fellowships for young faculty. She is a Sloan Fellow, MIT Technology Review TR-35 innovator, and recipient of the IJCAI Computers and Thought Award, NSF CAREER, Intel Rising Star Faculty award, Symantec Research Labs Fellowship, Rising Stars in EECS, Research Awards from Amazon/Facebook/Google, and best paper awards at multiple top machine learning and security conferences. Her research has been featured by major publications and media outlets such as Nature, Wired, New York Times, Fortune, and is on display at the Science Museum in London.


AI & Machine Learning

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


The mathematical foundations of computation, including algorithm design, complexity and logic
arrow-down-largearrow-left-largearrow-right-large-greyarrow-right-large-yellowarrow-right-largearrow-right-smallbutton-arrowclosedocumentfacebookfacet-arrow-down-whitefacet-arrow-downPage 1CheckedCheckedicon-apple-t5backgroundLayer 1icon-google-t5icon-office365-t5icon-outlook-t5backgroundLayer 1icon-outlookcom-t5backgroundLayer 1icon-yahoo-t5backgroundLayer 1internal-yellowinternalintranetlinkedinlinkoutpauseplaypresentationsearch-bluesearchshareslider-arrow-nextslider-arrow-prevtwittervideoyoutube