Dr. Ming-Hsuan Yang is a distinguished computer scientist and leading authority in computer vision and artificial intelligence with dual appointments at academia and industry. He currently serves as a Professor of Electrical Engineering and Computer Science at the University of California, Merced where he chairs the EECS Graduate Group, and as a Research Scientist at Google DeepMind. After receiving his PhD in Computer Science from the University of Illinois at Urbana-Champaign in 2000, he established his research foundation through studies at National Tsing Hua University in Taiwan, University of Southern California, and University of Texas at Austin. His professional trajectory includes a significant tenure as a Senior Research Scientist at the Honda Research Institute working on vision problems for humanoid robots before joining UC Merced in 2008, where he has since built a world-class research program in visual intelligence.
Dr. Yang's groundbreaking research has fundamentally transformed computer vision through his development of robust algorithms and standardized benchmarking methodologies that have become field standards. His seminal 2013 paper on large-scale evaluation of online object tracking algorithms established new frameworks for performance assessment across diverse scenarios, significantly advancing the state of the art in visual tracking technology. He pioneered the creation of globally recognized benchmark datasets including OTB and VOT, which have become essential resources for researchers worldwide, enabling consistent evaluation and comparison of tracking algorithms across the international research community. His innovative graph-based manifold ranking approach for saliency detection integrated foreground and background cues in novel ways, yielding substantial improvements in visual attention modeling that have influenced numerous subsequent advancements in computer vision systems used across multiple industries.
Beyond his technical contributions, Dr. Yang has been instrumental in fostering global collaboration within the computer vision community through his leadership roles as Program Chair of the IEEE International Conference on Computer Vision in 2019 and General Chair of the Asian Conference on Computer Vision in 2016. He currently serves as Associate Editor-in-Chief of IEEE Transactions on Pattern Analysis and Machine Intelligence, the premier journal in the field, while maintaining an active research laboratory at UC Merced that has produced numerous highly cited publications and trained the next generation of computer vision researchers. As a highly cited researcher from 2018 to 2024 and recipient of multiple prestigious honors including IEEE, ACM, and AAAI Fellowships, he continues to shape the future direction of artificial intelligence through his work bridging fundamental vision problems with practical applications in deep learning systems.
