Prof. Xiaodong Wang, IEEE Fellow, Columbia University, USA
Bio: Xiaodong Wang (S'98-M'98-SM'04-F'08) received the Ph.D degree in Electrical Engineering from Princeton University. He is a Professor of Electrical Engineering at Columbia University in New York. Dr. Wang's research interests fall in the general areas of computing, signal processing and communications, and has published extensively in these areas. Among his publications is a book entitled ``Wireless Communication Systems: Advanced Techniques for Signal Reception'', published by Prentice Hall in 2003. His current research interests include wireless communications, statistical signal processing, and machine learning. Dr. Wang received the 1999 NSF CAREER Award, the 2001 IEEE Communications Society and Information Theory Society Joint Paper Award, and the 2011 IEEE Communication Society Award for Outstanding Paper on New Communication Topics. He has served as an Associate Editor for the IEEE Transactions on Communications, the IEEE Transactions on Wireless Communications, the IEEE Transactions on Signal Processing, and the IEEE Transactions on Information Theory. He is a Fellow of the IEEE and listed as an ISI Highly-cited Author.
Speech Title: Radar – Communication Co-Existence – An Overview
Abstract: This talk explores coordinated and uncoordinated interference suppression methods- each distinguished by whether the constituent systems are able to share information and hence optimize aggregate performance| from both radar and communication-centric perspectives. First, we explore joint design of a multiple-input multiple-output (MIMO) radar with colocated antennas and a MIMO communication system for shared spectrum access. The design problem|to select the radar waveforms, the radar receive lter and the communication space-time codebook|is stated as the constrained maximization of the signal-to-interference-plus-noise ratio at the radar receiver, where interference is due to both clutter and the coexistence structure, subject to constraints on both systems. Moving targets and static targets are considered separately. Second, we apply an uncoordinated approach whereby interference suppression is integrated into the processing method used for the system's primary function. In particular we show, from a stepped-frequency radar perspective, that the communication interference manifests structure paving the way for sparse reconstruction; and from a communication perspective, that when the radar signals admit a sparse representation in a known dictionary, further sparsity can be imposed on the vector of demodulation errors to enable interference-robust symbol recovery. We describe two algorithms for sparse radar imaging withinterference suppression: an iterative optimization algorithm and a corresponding model-based deep learning architecture.
Prof. Erchin Serpedin, IEEE Fellow, Texas A&M University, USA
Bio: Erchin Serpedin (Fellow, IEEE) received the
specialization degree in transmission and processing of information from
Ecole Superieure D’Electricite (SUPELEC), Paris, France, in 1992, the M.Sc.
degree from the Georgia Institute of Technology, Atlanta, GA, USA, in 1992,
and the Ph.D. degree from the University of Virginia, Charlottesville, VA,
USA, in 1999.,He is a Professor with the Electrical and Computer Engineering
Department, Texas A&M University, College Station, TX, USA. He has authored
three research monographs, one textbook, 17 book chapters, 170 journal
papers, and 270 conference papers. His current research interests include
signal processing, machine learning, artificial intelligence, cyber
security, smart grids, and wireless communications.,Prof. Serpedin served as
an Associate Editor for more than 12 journals, including the IEEE
Transactions on Information Theory, the IEEE Transactions on Signal
Processing, the IEEE Transactions on Communications, IEEE Signal Processing
Letters, IEEE Communications Letters, the IEEE Transactions on Wireless
Communications, IEEE Signal Processing Magazine, and Signal Processing
(Elsevier), and as a technical chair for six major conferences. (Based on
document published on 24 February 2020).
Speech Title: Robust Signal Processing Algorithms for Detection of Metagenomic Biomarkers
Abstract: Recent advances in high-throughput sequencing technologies opened a new era in genomics, called metagenomics. Metagenomics presents itself as the standard approach for characterizing the compositional and functional capacity of microbial communities by direct study of the genetic contents recovered from environmental samples without prior culturing. Although these advancements enable researchers to sequence bacterial populations at a reasonable budget, analyzing these massive metagenomic datasets present significant challenges. This talk presents novel computational tools, based on signal processing and machine learning concepts, to enable the investigation of such biological systems. The key bioinformatics problem addressed herein talk concerns the robust identification of the potential metagenomic biomarkers, which play a critical role in understanding the biological processes under study and in developing possible therapies.
Prof. Graziano Chesi, IEEE Fellow, University of Hong Kong, Hong Kong, China
Bio: Graziano Chesi joined the Department of Electrical and
Electronic Engineering of the University of Hong Kong in 2006, where he is
now a full professor. He received the Laurea in Information Engineering from
the University of Florence in 1997 and the PhD in Systems Engineering from
the University of Bologna in 2001. He served as associate editor for various
journals, including Automatica, the European Journal of Control, the IEEE
Control Systems Letters, the IEEE Transactions on Automatic Control, the
IEEE Transactions on Computational Biology and Bioinformatics, and Systems
and Control Letters. He founded the Technical Committee on Systems with
Uncertainty of the IEEE Control Systems Society. He also served as chair of
the Best Student Paper Award Committees of the IEEE Conference on Decision
and Control and the IEEE Multi-Conference on Systems and Control. He
authored the books "Homogeneous Polynomial Forms for Robustness Analysis of
Uncertain Systems" (Springer 2009) and "Domain of Attraction: Analysis and
Control via SOS Programming" (Springer 2011). He was elevated to the grade
of IEEE Fellow for contributions to control of nonlinear and
multi-dimensional systems upon evaluation by the IEEE Control Systems
Speech Title: Multiple-View L2 Triangulation via Semidefinite Programming
Abstract: A key problem in computer vision consists of estimating the position in the scene of a point from the available estimates of its image projections on several cameras and the calibration parameters. This problem, known as multiple-view triangulation, has received a number of contributions given its importance. This talk explains how semidefinite programming, an area of convex optimization, can be exploited to address this problem in the common case where the reprojection error to be minimized is measured through the L2 norm. In particular, two methods are presented, one suitable for real time applications based on the fundamental matrices relating each pair of views, and the other more accurate based on the projection matrices that characterize each view. Both methods provide an estimate of the sought scene point together with a certificate of optimality. The talk also explains how occlusions can be considered in the presented framework.
Prof. Tao Zhang, North China University of Technology, China
Prof. Tony Zhang is a full professor in College of Information Technology, North China University of Technology (NCUT). He received his PhD degree in computer science from Kent State University of United States America. He was the Chief scientific adviser, North American Headquarters, Volkswagen Group, Germany, the senior banking consultant of IBM banking division, United States, the vice president of technology and operations, Bank of America, the CTO of PRECOM Information Technology, a Silicon Valley company. He founded the Wise-Code Information Technology Co., Ltd and has designed and developed more than 60 software products with independent intellectual property rights, and 40 published papers on the intelligence applications which have been widely used in the fields of intelligence finance and banking, intelligence government, smart education, intelligence medical cares, intelligence manufacturing and intelligence cities and communities, intelligence safety cities, intelligence travel and etc. He is currently the chief expert of the National Invested Top Important Intelligence Projects by Department of National Science and technology, National Recognized and Certified High Level Talents of Oversea Returned Scientist From Western and American, the overseas Senior Technical Adviser of the CBRC, the chief scientist of Beijing Aero space ChangFeng Co., Ltd., President of Electronic branch of Beijing Expert Association and Vice President of Western Returned Students' Club, the Chairman of International Conference in the Data Signal Process and Artificial Intelligence.
Speech Title: Intelligent Applications of Loan Risk Control Based on Financial Big Data Technology
Abstract: Intelligent Technology Applications of Loan Risk Control Based on Financial Big Data, Deep Learning, Machine Learning, Cloud Computation, and Block Chain technology are about how to prepare data warehouse, extract the features of data, draw the precise outline of potential customers who are going to borrow money, and build the models of risk control for borrowers so that lenders can decide can we lend our money to the customer, how much money to be landed to him, how much interest we can take from him before the loans can be granted. How do we control the risk during the loans of the customers, how to manage the risk after the loans. The speech will be talking about the algorithms applied onto the differen phases of processes of loans, data ETL, data classification and cluster analysis, data modelling and analysis, deep learning and machine learning. It will be talked about the architectures of information technology for the workable intelligent loan system implementation, business processes of the loans, strategy of the loans, scoring of borrower behavior, scoring of borrower credits, knowledge base, and decision & inference engine. The contents can be used to design a real system of loans.