Qingfu Zhang (张青富), City University of Hong Kong, Hong Kong, China
Ph.D. Professor, IEEE Fellow 

张青富教授现为香港城市大学电脑科学系计算智能讲座教授、长江学者讲座教授、IEEE Fellow。2016至2020连续入选Web of Science计算机科学领域的高被引学者,总引用超过两万次。主要从事智能计算、多目标优化及机器学习方面的研究。他提出的多目标分解算法框架MOEA/D已成为目前多目标进化计算领域最常用的两种框架之一。

Qingfu Zhang is a Professor at the Department of Computer Science, City University of Hong Kong. His main research interests include evolutionary computation, optimization, neural networks, data analysis, and their applications. His MOEA/D has been one of most researched and used multiobjective evolutionary algorithmic framework. He is currently leading the Metaheuristic Optimization Research Group in City University of Hong Kong. Professor Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions Cybernetics. He was awarded the 2010 IEEE Transactions on Evolutionary Computation Outstanding Paper Award. He is on the list of the Thomson Reuters 2016 highly cited researchers in computer science. He is a fellow of IEEE

Speech Title: Introduction to Decomposition Based Multiobjective Optimization
Multiobjective Optimization Evolutionary Algorithms (MOEA/D) have been a widely used and studied evolutionary multiobjective optimization (EMO)) algorithmic framework over the last few years. MOEA/D borrows ideas from traditional optimization. It decomposes a multiobjective problem into a number of subtasks, and then solves them in a collaborative manner.  MOEA/D provides a very natural bridge between multiobjective evolutionary algorithms and traditional decomposition methods. In this talk, I will explain the basic ideas behind MOEA/D and some recent developments. I will also outline some possible research issues in multiobjective evolutionary computation

Xiao Wu, Southwest Jiaotong University, China
Ph.D. Professor

Prof. Xiao Wu is a full Professor and the Assistant Dean of School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China. He received the B.Eng. and M.S. degrees in computer science from Yunnan University, Yunnan, China, in 1999 and 2002, respectively, and the Ph.D. degree in Computer Science from City University of Hong Kong, Hong Kong in 2008. He was with the Institute of Software, Chinese Academy of Sciences, Beijing, China, from 2001 to 2002. He was a Research Assistant and a Senior Research Associate at the City University of Hong Kong, Hong Kong, from 2003 to 2004, and 2007 to 2009, respectively. He was with the School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA, and at School of Information and Computer Science, University of California, Irvine, CA, USA as a Visiting Scholar during 2006 to 2007 and 2015 to 2016, respectively. He has authored or co-authored more than 70 research papers in well-respected journals, such as TIP, TMM, TMI, TITS and prestigious proceedings like CVPR, ICCV and ACM MM. He received the Second Prize of Natural Science Award of the Ministry of Education, China in 2016 and the Second Prize of Science and Technology Progress Award of Henan Province, China in 2017. His research interests include artificial intelligence, computer vision, multimedia information retrieval, and image/video computing.

Speech Title: Exploration of Deep Learning for Fashion Search and Recommendation

Abstract: With the exponential growth of e-commerce, online clothing shopping becomes more and more popular, which takes up a significant portion of the retail. Driven by the huge profit potential, clothing item retrieval has been received a great deal of attention in multimedia and computer vision communities. Meanwhile, deep learning has shown its promising ability in many fields of computer science, such as computer vision, natural language processing and multimedia. In this talk, we will push the frontier of fashion search and recommendation by bringing together techniques from computer vision and deep learning. We seeks to advance fashion studies and techniques, including fashion search, clothing image generation and fashion recommendation.

Huaiyu Dai, NC State University, USA
Ph.D. Professor, IEEE Fellow 

Huaiyu Dai (F’17) received the B.E. and M.S. degrees in electrical engineering from Tsinghua University, Beijing, China, in 1996 and 1998, respectively, and the Ph.D. degree in electrical engineering from Princeton University, Princeton, NJ in 2002.
He was with Bell Labs, Lucent Technologies, Holmdel, NJ, in summer 2000, and with AT&T Labs-Research, Middletown, NJ, in summer 2001. He is currently a Professor of Electrical and Computer Engineering with NC State University, Raleigh, holding the title of University Faculty Scholar. His research interests are in the general areas of communications, signal processing, networking, and computing. His current research focuses on machine learning and artificial intelligence for communications and networking, multilayer and interdependent networks, dynamic spectrum access and sharing, as well as security and privacy issues in the above systems.
He has served as an editor for IEEE Transactions on Communications, IEEE Transactions on Signal Processing, and IEEE Transactions on Wireless Communications. Currently he is an Area Editor in charge of wireless communications for IEEE Transactions on Communications, and a member of the Executive Editorial Committee for IEEE Transactions on Wireless Communications. He was a co-recipient of best paper awards at 2010 IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS 2010), 2016 IEEE INFOCOM BIGSECURITY Workshop, and 2017 IEEE International Conference on Communications (ICC 2017).

Speech Title: Recent Advances in Dynamic Processes over Networks
Many interesting phenomena happening around us can be described as dynamic processes over networks. A dynamic process on a network refers to any process whose state, represented by variables defined on network nodes, changes over time due to interactions among the nodes according to certain rules. One prominent example is spread of infectious diseases though networks of human contact, whose study dates back to almost one century ago, but draws close attention nonetheless these days in the face of COVID-19. News or gossips are similarly spread through social interactions, increasingly over the social platforms. Such information dynamics can dramatically influence individuals’ perception and shape their behavior, the understanding of which has become increasingly important for modern politics and commercial marketing. On the technological side, how to disseminate information quickly and efficiently over large-scale complex networks, and how to prevent the propagation of cascading failures over interdependent networks, are two important questions that network scientists and engineers endeavor to address. This talk will give an overview of these interesting problems, illustrate the common models and analytical tools used for their studies, and discuss recent advances in this area.


Chong-Yung Chi, National Tsing Hua University, Taiwan
Ph.D. Professor, IEEE Fellow 

Chong-Yung Chi received Ph.D. degree from the University of Southern California, Los Angeles, California, in 1983 all in Electrical Engineering. Currently, he is Professor of National Tsing Hua University, Hsinchu, Taiwan. He has published more than 240 technical papers, including more than 85 journal papers (mostly in IEEE Trans. Signal Processing), more than 140 peer-reviewed conference papers, 3 book chapters, and 2 books, including a recent textbook, Convex Optimization for Signal Processing and Communications from Fundamentals to Applications, CRC Press, 2017 (which has been popularly used in a series of invited intensive short courses at the top-ranking universities in Mainland China since 2010 before its publication). He received 2018 IEEE Signal Processing Society Best Paper Award, entitled “Outage Constrained Robust Transmit Optimization for Multiuser MISO Downlinks: Tractable Approximations by Conic Optimization,” IEEE Tran. Signal Processing, vol. 62, no. 21, Nov. 2014. His current research interests include signal processing for wireless communications, convex analysis and optimization for blind source separation, biomedical and hyperspectral image analysis, and graph signal processing.
He is an IEEE Fellow. He has been a Technical Program Committee member for many IEEE sponsored and co-sponsored workshops, symposiums and conferences on signal processing and wireless communications, including Co-organizer and General Co-chairman of 2001 IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC). He was an Associate Editor (AE) for four IEEE Journals, including IEEE Trans. Signal Processing for 9 years (5/2001~4/2006, 1/2012~12/2015), and he was a member of Signal Processing Theory and Methods Technical Committee (SPTM-TC) (2005-2010), a member of Signal Processing for Communications and Networking Technical Committee (SPCOM-TC) (2011-2016), and a member of Sensor Array and Multichannel Technical Committee (SAM-TC) (2013-2018), IEEE Signal Processing Society.

Secrecy Energy Efficiency in Cognitive Radio Networks with Untrusted Secondary Users
The information security and energy efficiency in cognitive radio (CR) networks have been extensively studied. However, the practical scenario involving multiple untrusted secondary users (SUs) in CR networks under the underlay spectrum sharing mechanism has not been studied so far. This talk considers the downlink secrecy energy efficient coordinated beamforming design for multiple inputs single output CR networks under this scenario. Our goal is to maximize the global secrecy energy efficiency (GSEE), defined as the ratio of the sum of secrecy rates of all the primary users (PUs) to the total power consumption, under requirements on quality of service of Pus and SUs as well as constraints on power budget at the primary transmitter (PTx) and the secondary transmitter (STx). To tackle the non-convex GSEE maximization (GSEEM) problem, an algorithm is proposed based on Dinkelbach method and successive convex approximation to jointly optimize beamforming vectors of the PTx and the STx. The convergence behavior and the computational complexity of the proposed GSEEM algorithm are analyzed, followed by the connection with the secrecy rate maximization design and the power minimization (PM) design in terms of GSEE. In view of significantly higher computational complexity of the proposed GSEEM algorithm than that of the PM design, a 2-step searching scheme is further designed to efficiently search for an approximate solution to the considered GSEEM problem based on the PM design and the golden search method. Simulation results demonstrate the efficacy of the proposed GSEEM algorithm and the searching scheme, and show that the spatial degrees of freedom (primarily determined by the antenna numbers of PTx and STx) is the key factor to the performance of the proposed GSEEM algorithm.