Academic Profile : Faculty
Prof Chen Change Loy
Professor, College of Computing & Data Science
President's Chair in Computer Science
Deputy Director for Centre of AI-for-X, Others - Please update the Remarks field
NTU Co-Associate Lab Director, S-Lab for Advanced Intelligence
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Chen Change Loy is a President's Chair Professor with the College of Computing and Data Science, Nanyang Technological University, Singapore. He received his PhD (2010) in Computer Science from the Queen Mary University of London. Prior to joining NTU, he served as a Research Assistant Professor at the MMLab of the Chinese University of Hong Kong, from 2013 to 2018.
His research interests include computer vision and deep learning with a focus on image/video restoration and enhancement, generative tasks, and representation learning. He and his research group pioneer the research in face detection, face alignment, and image super-resolution by deep learning. His journal paper on image super-resolution was selected as the `Most Popular Article' by IEEE Transactions on Pattern Analysis and Machine Intelligence in 2016. It remains as one of the top 10 non-survey articles to date.
He is recognized as one of the 100 most influential scholars in computer vision from 2020 to 2023 by AMiner. He received the Nanyang Associate Professorship Award in 2019. He was selected as the outstanding reviewer of ACCV 2014, BMVC 2017, and CVPR 2017.
He serves as an Associate Editor of the Computer Vision and Image Understanding (CVIU), International Journal of Computer Vision (IJCV) and IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). He also serves/served as the Area Chair of top conferences such as ICCV, CVPR, ECCV, ICLR and NeurIPS. He will serve as the Program Co-Chair of CVPR 2026. He has co-organized several workshops and challenges at major computer vision conferences. He is a senior member of IEEE.
Check out MMLab@NTU.
His research interests include computer vision and deep learning with a focus on image/video restoration and enhancement, generative tasks, and representation learning. He and his research group pioneer the research in face detection, face alignment, and image super-resolution by deep learning. His journal paper on image super-resolution was selected as the `Most Popular Article' by IEEE Transactions on Pattern Analysis and Machine Intelligence in 2016. It remains as one of the top 10 non-survey articles to date.
He is recognized as one of the 100 most influential scholars in computer vision from 2020 to 2023 by AMiner. He received the Nanyang Associate Professorship Award in 2019. He was selected as the outstanding reviewer of ACCV 2014, BMVC 2017, and CVPR 2017.
He serves as an Associate Editor of the Computer Vision and Image Understanding (CVIU), International Journal of Computer Vision (IJCV) and IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). He also serves/served as the Area Chair of top conferences such as ICCV, CVPR, ECCV, ICLR and NeurIPS. He will serve as the Program Co-Chair of CVPR 2026. He has co-organized several workshops and challenges at major computer vision conferences. He is a senior member of IEEE.
Check out MMLab@NTU.
Dr Loy's research interests include computer vision and deep learning with a focus on image/video restoration and enhancement, creative content generation, and representation learning.
Links:
Personal Homepage
MMLab@NTU
Google Scholar Profile
Links:
Personal Homepage
MMLab@NTU
Google Scholar Profile
- Google PhD Fellowship
- Deep Generative Modeling of 3D Data
- 3D Geometry and Semantic Modeling for Human‐Scene Interaction
- President's Chair in Computer Science
- Self-Supervised Learning For Fine-Grained Classification
- Resource-Efficient AI “Lightweight model design for video restoration, Learn video restoration from limited data, Efficient blind video restoration”
- Resource-Efficient AI “Human AI Co-Design, Resource Efficient Content Creation, AI Understanding of Design and Creation”
- Collaborative AI “Human-AI collaborative systems for multi-media content creation, Intelligent systems to improve human's decision with clear interpretation”
- Resource-Efficient AI “AI Understanding of Creation and Design, Learning from Small Datasets”
- Open-vocabulary visual recognition, Scene understanding, Foundation models
- Investigating Intrinsic Image Properties in Modern Generative Networks for Enhanced Image Generation