Professor Yu, Yizhou

PhD UC Berkeley
Chair Professor

Tel: (+852) 2857 8238
Fax: (+852) 2559 8447

Professor Yu received his PhD degree in computer science from University of California at Berkeley. He was first a tenure-track then a tenured professor at University of Illinois, Urbana-Champaign (UIUC) for twelve years. He has also served as the director of AI Lab in the Department of Computer Science at the University of Hong Kong. He is an IEEE Fellow.

Professor Yu has made important contributions to AI and visual computing, including deep learning, computer vision, computer graphics, and VR/AR. Technologies co-invented by him have been frequently adopted by the film and healthcare industries. He is a recipient of US National Science Foundation CAREER Award, and has been named 2022 World's Top 2% Scientists by Stanford University. He has delivered keynote speeches at many conferences, including Asia Conference on Machine Learning and Computing 2019, International Conference on Digital Home 2012, and Pacific Graphics 2007. His current research includes artificial intelligence foundation models, AI based multimedia content generation, and computer vision.

Prof Yu has served on the editorial board of IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and IEEE Transactions on Visualization and Computer Graphics. He has also served on the program committee of many leading international conferences, including CVPR, NeurIPS and SIGGRAPH, and as a program chair of multiple conferences, including the International Conference on Computer Animation and Social Agents.

Research Interests

Artificial Intelligence (AI), Computer Vision, Machine Learning, Multimedia

Selected Publications

  • HY Zhou, X Chen, Y Zhang, R Luo, L Wang, and Y Yu. Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports. NATURE Machine Intelligence, Vol 4, 2022.
  • HY Zhou, Y Yu, C Wang et al. A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. NATURE Biomedical Engineering, Vol 7, 2023.
  • C Chen, J Li, HY Zhou, X Han, Y Huang, X Ding, and Y Yu. Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol 45, No 3, 2023.
  • H Ma, X Lin, and Y Yu. I2F: A Unified Image-to-Feature Approach for Domain Adaptive Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022.
  • G Zhao, Q Feng, C Chen, Z Zhou, and Y Yu. Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol 44, No 11, 2022.
  • S Yang, G Li, and Y Yu. Relationship-Embedded Representation Learning for Grounding Referring Expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol 43, No 8, 2021.
  • C Fang, Z Liao, and Y Yu. Piecewise Flat Embedding for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Vol 41, No 6, 2019.
  • W Ge, B Gong, and Y Yu. Image Super-Resolution via Deterministic-Stochastic Synthesis and Local Statistical Rectification. ACM Transactions on Graphics, Vol 37, No 6, 2018 (SIGGRAPH Asia 2018).
  • X Han, C Gao, and Y Yu. DeepSketch2Face: A Deep Learning Based Sketching System for 3D Face and Caricature Modeling. ACM Transactions on Graphics, Vol 36, No 4, 2015 (SIGGRAPH 2017).
  • S Bi, X Han, and Y Yu. An L1 Image Transform for Edge-Preserving Smoothing and Scene-Level Intrinsic Decomposition. ACM Transactions on Graphics, Vol 34, No 4, 2015 (SIGGRAPH 2015).