APSIPA U.S. Local Chapter will organize two seminars at 4:00 – 5:40 pm PST Pacific Time (California), December 6, 2022 (Tuesday)
Seminar 1: Green Learning: Methodology, Examples and Perspectives
Speaker: Prof. C.-C. Jay Kuo, University of Southern California, USA
The rapid advanced in artificial intelligence in the last decade has primarily been built upon the wide applications of deep learning (DL). Yet, the high carbon footprint yielded by larger DL networks becomes a concern to sustainability. Green learning (GL) has been proposed as an alternative learning paradigm to address this concern. GL is characterized by low carbon footprints, small model sizes, low computational complexity, and mathematical transparency. GL offers energy-effective solutions in cloud centers as well as mobile/edge devices.
Algorithmically, GL has three main building blocks: representation learning, feature learning, and decision learning. It has been successfully applied to quite a few applications. This talk provides an overview on GL’s mathematical foundation, computational algorithms and demonstrated applications. Furthermore, it will offer an outlook for future research and development opportunities. Finally, the connection between GL and DL will also be discussed.
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 and an Academician of Academia Sinica.
Dr. Kuo has received numerous awards for his 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 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 164 students to their PhD degrees and supervised 31 postdoctoral research fellows.
Seminar 2: Making the Invisible Visible: Toward High-Quality Physics-Guided THz Computational Imaging
Speaker: Prof. Chia-Wen Lin, National Tsing Hua University, Taiwan
Terahertz (THz) computational imaging has recently attracted significant attention thanks to its non-invasive, non-destructive, non-ionizing, material-classification, and ultra-fast nature for 3D object exploration and inspection. However, its strong water absorption nature and low noise tolerance lead to undesired blurs and distortions of reconstructed THz images. The performances of existing methods are highly constrained by the diffraction-limited THz signals. In this talk, we will introduce the characteristics of THz imaging and its applications. We will also show how to break the limitations of THz imaging with the aid of rich spectral amplitude and phase information carried in prominent THz frequencies (i.e., the water absorption profile of THz signal) for THz image restoration. To this end, we propose a novel physics-guided deep neural network model, namely Subspace-Attention-guided Restoration Network (SARNet), that fuses such multi-spectral features of THz images for effective restoration. Furthermore, we experimentally construct an ultra-fast THz time-domain spectroscopy system covering a broad frequency range from 0.1 THz to 4 THz for building up temporal/spectral/spatial/phase/material THz database of hidden 3D objects.
Prof. Chia-Wen Lin is currently a Professor with the Department of Electrical Engineering, National Tsing Hua University (NTHU), Taiwan. He also serves as Deputy Director of the AI Research Center of NTHU. His research interests include image/video processing, computer vision, and video networking.
Dr. Lin is an IEEE Fellow, and has served on IEEE Circuits and Systems Society (CASS) Fellow Evaluating Committee since 2021. He is also serving as IEEE CASS BoG Members-at-Large during 2022-2024. He was Steering Committee Chair of IEEE ICME (2020-2021), IEEE CASS Distinguished Lecturer (2018-2019), and President of the Chinese Image Processing and Pattern Recognition (IPPR) Association, Taiwan (2019-2020). He has served as Associate Editor of IEEE Transactions on Image Processing, IEEE Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, and IEEE Multimedia. He also served as a Steering Committee member of the IEEE Transactions on Multimedia. He was Chair of the Multimedia Systems and Applications Technical Committee of the IEEE CASS. He served as TPC Chair of IEEE ICME in 2010 and IEEE ICIP in 2019, and the Conference Chair of IEEE VCIP in 2018. His papers won the Best Paper Award of IEEE VCIP 2015, and the Young Investigator Award of VCIP 2005.
Host: Professor Nam Ling, Santa Clara University, USA
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Meeting ID: 973 6863 1313
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Meeting ID: 973 6863 1313
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