Professor C.-C. Jay Kuo
Miroslav Krstic

William M. Hogue Professor,

Distinguished Professor of Electrical and Computer Engineering and Computer Science

Director of the Media Communications Laboratory

University of Southern California, USA


Dr. C.-C. Jay Kuo received his Ph.D. degree from the Massachusetts Institute of Technology in 1987. He is now with the University of Southern California (USC) as William M. Hogue Professor, Distinguished Professor of Electrical and Computer Engineering and Computer Science, and Director of the Media Communications Laboratory. His research interests are in visual computing and communication. He is a Fellow of AAAS, NAI, IEEE and SPIE. Dr. Kuo has received numerous awards for his outstanding research contributions, including the 2010 Electronic Imaging Scientist of the Year Award, the 2010-11 Fulbright-Nokia Distinguished Chair in Information and Communications Technologies, the 2019 IEEE Computer Society Edward J. McCluskey Technical Achievement Award, the 2019 IEEE Signal Processing Society Claude Shannon-Harry Nyquist Technical Achievement Award, the 2020 IEEE TCMC Impact Award, the 72nd annual Technology and Engineering Emmy Award (2020), and the 2021 IEEE Circuits and Systems Society Charles A. Desoer Technical Achievement Award. Dr. Kuo was Editor-in-Chief for the IEEE Transactions on Information Forensics and Security (2012-2014) and the Journal of Visual Communication and Image Representation (1997-2011). He is currently the Editor-in-Chief for the APSIPA Trans. on Signal and Information Processing (2022-2023). He has guided 161 students to their PhD degrees and supervised 31 postdoctoral research fellows.


Green Learning for Mobile Computing


There has been a rapid development of artificial intelligence and machine learning applications in the last decade. The core lies in a large amount of annotated training data and deep learning networks. Since deep learning solutions demand expensive GPUs and huge power consumption, it is difficult to implement them in mobile and edge devices. There have been efforts to simplify deep learning architectures and model sizes and fit them into mobile devices. Yet, the gap is still big. Actually, it could be more effective to build a new learning paradigm that is power efficient algorithmically. This emerging solution is called “green learning”. I have been devoted to green learning since 2014. The technology has become more mature nowadays. Green learning contains several innovative ideas. It contains neither neurons nor networks. Instead, it is built upon three components: 1) unsupervised representation learning, 2) supervised feature learning, and 3) classifiers/regressors. Green learning has been successfully applied to image classification, point cloud classification, segmentation, registration, texture synthesis, face verification and gender classification, anomaly localization, super resolution, etc. Performance comparison between deep learning and green learning for several applications will be presented to demonstrate the potential of green learning.