Keynote & Plenary Speakers  主讲嘉宾 (ICSPS 2020)

Prof. Robert Minasian
Life Fellow & OSA Fellow
The University of Sydney, Australia

Professor Minasian is a Chair Professor with the School of Electrical and Information Engineering at the University of Sydney, Australia. He is also the Founding Director of the Fibre-optics and Photonics Laboratory. His research has made key contributions to microwave photonic signal processing. He is recognized as an author of one of the top 1% most highly cited papers in his field worldwide. Professor Minasian has contributed over 388 research publications, including Invited Papers in the IEEE Transactions and Journals. He has 76 Plenary, Keynote and Invited Talks at international conferences. He has served on numerous technical and steering committees of international conferences. Professor Minasian was the recipient of the ATERB Medal for Outstanding Investigator in Telecommunications, awarded by the Australian Telecommunications and Electronics Research Board. He is a Life Fellow of the IEEE, and a Fellow of the Optical Society of America.
 

 

 

Prof. Vivek Goyal
IEEE Fellow & OSA Fellow
Boston University, USA

Vivek Goyal received the M.S. and Ph.D. degrees in electrical engineering from the University of California, Berkeley, where he received the Eliahu Jury Award for outstanding achievement in systems, communications, control, or signal processing. He was a Member of Technical Staff at Bell Laboratories, a Senior Research Engineer for Digital Fountain, and the Esther and Harold E. Edgerton Associate Professor of Electrical Engineering at MIT. He was an adviser to 3dim Tech, winner of the 2013 MIT $100K Entrepreneurship Competition Launch Contest Grand Prize, and consequently with Nest Labs 2014-2016. He is now a Professor of Electrical and Computer Engineering at Boston University. Dr. Goyal is a Fellow of the IEEE and of the OSA. He was awarded the IEEE Signal Processing Society (SPS) Magazine Award in 2002, the IEEE Int. Conf. on Image Processing Best Paper Award in 2014, and the IEEE SPS Best Paper Award in 2017 and 2019. Work he supervised won awards at the IEEE Data Compression Conf. in 2006 and 2011, the IEEE Sensor Array and Multichannel Signal Processing Workshop in 2012, the IEEE Int. Conf. on Imaging Processing in 2018, and the IEEE Int. Conf. Computational Photography in 2018, as well as five MIT thesis awards. He is a co-author of Foundations of Signal Processing (Cambridge University Press, 2014).

Speech Title: One Click at a Time: Photon- and Electron-Level Modeling for Improved Imaging
Abstract: Detectors that are capable of sensing a single photon are no longer rare, yet the bulk of signal processing intuitions and methods have implicit connections with Gaussian noise models. Particle-level modelling can lead to substantially different methods that sometimes perform dramatically better than classical methods. For example, using detectors with single-photon sensitivity enables lidar systems to form depth and reflectivity images at very long ranges. Initially, our interest was in exploiting inhomogeneous Poisson process models and the typical structure of natural scenes to achieve extremely high photon efficiency. However, modelling at the level of individual photons does not merely give advantages when signals are weak. It is also central to withstanding high levels of ambient light and mitigating the effects of detector dead time, which ordinarily create high bias in high-flux imaging. Our sensor signal processing advances thus potentially improve lidar performance in settings with very high dynamic range of optical flux, such as navigation of autonomous vehicles. Modelling of dead time can presumably improve many other applications of time-correlated single-photon counting. Furthermore, modelling at the level of individual incident particles and emitted secondary electrons leads to improvements in focused ion beam microscopy that apply uniformly over all dose levels.

 

 

Prof. Alejandro F Frangi
IEEE Fellow & SPIE Fellow
University of Leeds, UK

Prof. Frangi is the Diamond Jubilee Chair in Computational Medicine at the University of Leeds with joint appointments in the Schools of Computing and the School of Medicine. He holds a Royal Academy of Engineering Chair in Emerging Technologies and is a Honorary Professor at KU Leuven, Leuven, Belgium. Prof Frangi graduated in BSc/MSc Telecommunications Engineering (1996) from the Universitat Politénica de Catalunya (Barcelona, Spain) with a thesis on image noise properties in Electrical Impedance Tomography. He was awarded a PhD in Imaging Sciences (2001) by University Medical Centre Utrecht (Utrecht, The Netherlands). His research focused on model-based cardiovascular image analysis. One of his contributions is the well-known vesselness filter, which is a classic in medical image computing. Since his PhD, he develop research interests at the interface between imaging and modelling using both data-driven (AI/ML) approaches and biomechanistic computational modelling. He was Visiting Scholar of the Chinese Academy of Sciences within the Presidential International Fellow Initiative and is currently a Pengcheng Visiting Scholar at the School of Biomedical Engineering in Shenzhen University. Prof Frangi has been an IEEE Member throughout his career starting as Student Member (1993) and becoming IEEE Fellow (2014). He was awarded the Early Career Award (2006) by the IEEE Engineering in Medicine and Biology Society and was General Chair of IEEE ISBI 2012 (Barcelona). He is also served as Chair of the EMB Society Fellow Nomination Committee (2018-2019) and is currently Chair of the IEEE Signal Processing Society Biomedical Imaging and Signal Processing Technical Committee. He was made SPIE Fellow in 2020.

Speech Title: Large-scale Multi-phenomics Image Computing and Beyond
Abstract:
Changes in worldwide demographics and modern lifestyles affect the human health in complex and systemic ways. Patient management requires an understanding the interplay between subsystems involved. Discovery of novel mechanistic pathways of disease across various organ systems emphasizes the need to interrogate the body across relevant imaging and sensing modalities and to account for genetic and omic information.
Integration and fusion of multi-modal and multi-organ cues is increasingly important so physicians can take most informed decisions. In addition, the availability of population health data opens up new challenges in data analytics yet will allow establishing phenotypic charts to put data from individual subjects in the wider population context. The ultimate goal of in vivo targeting of multiple organs simultaneously is to guide therapeutic development and personalise treatment.
This talk will present some of the progress made by our group in this space and will introduce some of the emerging challenges and opportunities for large-scale data analytics and integration, and for artificial intelligence and machine learning.

 

 

Prof. Min Wu
IEEE Fellow & AAAS Fellow & NAI Fellow
University of Maryland, College Park, USA

Min Wu is a Professor of Electrical and Computer Engineering and a Distinguished Scholar-Teacher at the University of Maryland, College Park. She is currently serving as Associate Dean for Graduate Affairs for the University’s Clark School of Engineering. She received the B.E. degree in electrical engineering -- automation and the B.A. degree in economics from Tsinghua University, Beijing, China, in 1996 with the highest honors, and her Ph.D. degree in electrical engineering from Princeton University in 2001. At UMD, she leads the Media and Security Team (MAST), with main research interests on information security and forensics, multimedia signal processing, and applications of data science and machine learning in health and IoT. Her research and education have been recognized by a U.S. NSF CAREER award, a TR100 Young Innovator Award from the MIT Technology Review, a U.S. ONR Young Investigator Award, a Computer World "40 Under 40" IT Innovator Award, an IEEE Harriett B. Rigas Education Award, an IEEE Distinguished Lecturer recognition, and several paper awards from IEEE SPS, ACM, and EURASIP. She was elected as IEEE Fellow, AAAS Fellow, and Fellow of the National Academy of Inventors for contributions to multimedia security and forensics. Dr. Wu chaired the IEEE Technical Committee on Information Forensics and Security (2012-2013), and has served as Vice President - Finance of the IEEE Signal Processing Society (2010-2012) and Editor-in-Chief of the IEEE Signal Processing Magazine (2015-2017). [URL: http://www.ece.umd.edu/~minwu/bio.html]

Speech Title: Exploiting Micro-Signals for Information Forensics
Abstract:
A variety of nearly invisible "micro-signals" have played important roles in media security and forensics. These noise-like micro-signals are ubiquitous and typically an order of magnitude lower in strength or scale than the dominant ones, although they are traditionally removed or ignored as nuances outside the forensic domain. In this talk, I will give examples of micro-signals used in information forensic research, and discuss the recent research trend harnessing micro-signals for wellness and healthcare. I will show in this talk how the expertise with micro-signals has enabled our research group to explore the new opportunities in physiological forensics and a broad range of applications.

 

 

Prof. Chang Wen Chen
IEEE Fellow & SPIE Fellow

University at Buffalo, State University of New York, USA

Chang Wen Chen is currently Presidential Chair Professor at The Chinese University of Hong Kong, Shenzhen. He continues to serve as an Empire Innovation Professor of Computer Science and Engineering at the University at Buffalo, State University of New York. He was Allen Henry Endow Chair Professor at the Florida Institute of Technology from July 2003 to December 2007. He was on the faculty of Electrical and Computer Engineering at the University of Rochester from 1992 to 1996 and on the faculty of Electrical and Computer Engineering at the University of Missouri-Columbia from 1996 to 2003.

He has been the Editor-in-Chief for IEEE Trans. Multimedia from January 2014 to December 2016. He has also served as the Editor-in-Chief for IEEE Trans. Circuits and Systems for Video Technology from January 2006 to December 2009. He has been an Editor for several other major IEEE Transactions and Journals, including the Proceedings of IEEE, IEEE Journal of Selected Areas in Communications, and IEEE Journal of Emerging and Selected Topics in Circuits and Systems. He is an IEEE Fellow since 2004 and an SPIE Fellow since 2007.  

 

 

Prof. Feifei Gao
IEEE Fellow

Tsinghua University, China

Feifei Gao received the B.Eng. degree from Xi’an Jiaotong University, China in 2002, the M.Sc. degree from McMaster University, Canada in 2004, and the Ph.D. degree from National University of Singapore in 2007. He was a Research Fellow with the Institute for Infocomm Research (I2R), A*STAR, Singapore in 2008 and an Assistant Professor with the School of Engineering and Science, Jacobs University, Bremen, Germany from 2009 to 2010. In 2011, he joined the Department of Automation, Tsinghua University, China, where he is currently an Associate Professor.

Prof. Gao's research interest include signal processing for communications, array signal processing, convex optimizations, and artificial intelligence assisted communications. He has authored/ coauthored more than 150 refereed IEEE journal papers and more than 150 IEEE conference proceeding papers that are cited more than 8300 times in Google Scholar. Prof. Gao has served as an Editor of IEEE Transactions on Wireless Communications, IEEE Journal of Selected Topics in Signal Processing (Lead Guest Editor), IEEE Transactions on Cognitive Communications and Networking, IEEE Signal Processing Letters, IEEE Communications Letters, IEEE Wireless Communications Letters, and China Communications. He has also serves as the symposium co-chair for 2019 IEEE Conference on Communications (ICC), 2018 IEEE Vehicular Technology Conference Spring (VTC), 2015 IEEE Conference on Communications (ICC), 2014 IEEE Global Communications Conference (GLOBECOM), 2014 IEEE Vehicular Technology Conference Fall (VTC), as well as Technical Committee Members for more than 50 IEEE conferences.

Speech Title: Deep Learning for Physical Layer Communications: An Attempt towards 6G
Abstract: Merging artificial intelligence in to the system design has appeared as a new trend in wireless communications areas and has been deemed as one of the 6G technologies. In this talk, we will present how to apply the deep neural network (DNN) for various aspects of physical layer communications design, including the channel estimation, channel prediction, channel feedback, data detection, and beamforming, etc. We will also present a promising new approach that is driven by both the communications data and the communication models. It will be seen that the DNN can be used to enhance the performance of the existing technologies once there is model mismatch. More interestingly, we will show that applying DNN can deal with the conventionally unsolvable problems, thanks to the universal approximation capability of DNN. With the well defined propagation model in communication areas, we also attempt to explain the DNN under the scenario of channel estimation and reach a strong conclusion that DNN can always provide the asymptotically optimal channel estimations. In all, DNN is shown to be a very powerful tool for communications and would make the communications protocols more intelligently. Nevertheless, as a new born stuff, one should carefully select suitable scenarios for applying DNN rather than simply spreading it everywhere.

 

 

Prof. Mads Græsbøll Christensen
Aalborg University, Denmark

Mads Græsbøll Christensen received the M.Sc. and Ph.D. degrees in 2002 and 2005, respectively, from Aalborg University (AAU) in Denmark, where he is also currently employed at the Dept. of Architecture, Design & Media Technology as Full Professor in Audio Processing and is head and founder of the Audio Analysis Lab. He was formerly with the Dept. of Electronic Systems at AAU and has held visiting positions at Philips Research Labs, ENST, UCSB, and Columbia University. He has published 4 books and more than 200 papers in peer-reviewed conference proceedings and journals and has given tutorials at major conferences such as ICASSP, EUSIPCO, and INTERSPEECH and a keynote talk at IWAENC. His research interests lie in audio and acoustic signal processing where he has worked on topics such as microphone arrays, noise reduction, signal modeling, speech analysis, audio classification, and audio coding. Dr. Christensen has received several awards for his work, including best paper awards, the Spar Nord Foundation’s Research Prize, a Danish Independent Research Council Young Researcher’s Award, the Statoil Prize, and the EURASIP Early Career Award. He has received major grants from Independent Research Fund Denmark, the Villum Foundation, and Innovation Fund Denmark. Currently, he serves as Editor-in-Chief of EURASIP Journal on Audio, Speech, and Music Processing and as Senior Area Editor of IEEE Signal Processing Letters, and he has previously served as Associate Editor of IEEE/ACM Trans. on Audio, Speech, and Language Processing and IEEE Signal Processing Letters. He is a member of the IEEE Audio and Acoustic Signal Processing Technical Committee, and a founding member of the EURASIP Special Area Team in Acoustic, Speech and Music Signal Processing. He is Senior Member of the IEEE, Member of EURASIP, and Member of the Danish Academy of Technical Sciences.

Speech Title: Signal-Adaptive and Perceptually Optimized Sound Zones
Abstract:
Sound zones is a promising concept for delivering individualized contents in different parts of an acoustic environment, called zones, without leakage. Its many uses include home entertainment systems, cars, museums, and concerts. In this talk, I will present my lab's research on a general optimal filtering framework for sound zone generation, including the most recent results. This optimal filtering framework, which is based on the variable span linear filters known from speech enhancement, has a number of notable features which will be discussed, namely that it allows for explicit control over the trade-off between acoustic contrast and reconstruction error and for signal-adaptive perceptual weighting of the contrast and the reconstruction error such that the masking properties of the human auditory system are taken into account and exploited, much like in audio coding. Moreover the framework includes state-of-the-art methods such as pressure matching and acoustic contrast control as special cases. In the talk, the properties of the resulting filters will be explored and discussed in detail, and the most recent results, which include a subjective listening test comparing the variable span linear filters to the state of the art, will be presented. Finally, future work and open problems will be discussed.