Chenliang Xu

Chenliang Xu joined the faculty at the University of Rochester's Hajim School of Engineering and Applied Sciences in 2016 after receiving his PhD from the University of Michigan. His research interests include computer vision, robot perception, and artificial intelligence, and tackles interdisciplinary topics, including video understanding, audio-visual learning, vision and language, and methods for trustworthy AI. His work has focused primarily on the problems in high-level video understanding, such as video segmentation, activity recognition, and vision and language.

Xu is a recipient of the James P. Wilmot Distinguished Professorship (2021), the University of Rochester Research Award (2021), the "Best Paper Award" at the 17th ACM SIGGRAPH VRCAI Conference (2019), the "Best Paper Award" at the 14th Sound and Music Computing Conference (2017), and the University of Rochester AR/VR Pilot Award (2017). He has authored more than 100 peer-reviewed papers in computer vision, machine learning, multimedia, and AI venues. He served as an associate editor for IEEE Transactions on Multimedia and an area chair/reviewer for various international conferences.

At the University of Michigan, his dissertation advanced the uses of supervoxel hierarchies as a new type of generic video representation in video analysis. He built the first framework for streaming hierarchical segmentation of arbitrarily long videos with constant memory. He is currently working on a book project on video segmentation.