Prof. Qingfu Zhang (张青富) IEEE Fellow
City University of Hong Kong, Hong Kong
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: Recent Progress on MOEA/D
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.
Prof. Steven Guan
Xi'an Jiaotong-Liverpool University, China
Steven Guan received his M.Sc. & Ph.D. from the University of North Carolina at Chapel Hill. He is currently a professor in the computer science and software engineering department at Xi'an Jiaotong-Liverpool University (XJTLU). He served the head of department position at XJTLU for 4.5 years, creating the department from scratch and now in shape. Before joining XJTLU, he was a tenured professor and chair in intelligent systems at Brunel University, UK.
Prof. Guan has worked in a prestigious R&D organization for several years, serving as a design engineer, project leader, and department manager. Later he joined the Electrical & Computer Engineering Department at National University of Singapore as an associate professor.
Prof. Guan's research interests include: machine learning, modeling, security, networking, and pseudorandom number generation. He has published extensively in these areas, with 130 journal papers and 170+ book chapters or conference papers. He has chaired and delivered keynote speech for 40+ international conferences and served in 130+ international conference committees and 20+ editorial boards.
Input Space Partitioning for Machine Learning
Abstract: This talk introduces an input attribute grouping method to improve the performance of learning. During training for a specific problem, the input attributes are partitioned into groups according to the degree of inter-attribute promotion or correlation that quantifies the supportive or negative interactions between attributes. After partitioning, multiple sub-networks are trained by taking each group of attributes as their respective inputs. The final classification result is obtained by integrating the results from each sub-network. Experimental results on several UCI datasets demonstrate the effectiveness of the proposed method..
Assoc. Prof. Chee Wei Tan
College of Science and Engineering, City University of Hong Kong, Hong Kong
Dr. Chee Wei Tan is an Associate Professor at the City University of Hong Kong. He received his M.A. and Ph.D. degree from Princeton University. Dr. Tan was the recipient of the Princeton University Gordon Wu Prize for Excellence in 2008 and was twice selected to participate at the USA National Academy of Engineering China-America Frontiers of Engineering Symposium. His research interests include networks, statistical inference in data analytics, cyber-security, information theory, optimization theory and its applications. He serves as an Editor of the IEEE Transactions on Communications and an Editor of the IEEE/ACM Transactions on Networking.
Network Inference for Cyber Security in Online Social Networks
Abstract: Online social networks represent a fundamental medium for the spreading and diffusion of various information where the actions of certain users increase the susceptibility of other users to the same; this results in the successive spread of information from a small set of initial users to a much larger set. Examples include the spread of malicious rumors and Internet hoax. It is important to root out the source and have timely quarantine in order to enhance the network cyber security. This talk will focus on the mathematical theories and algorithms of network inference for cyber-security in online social networks by leveraging ideas in graph theory and statistical inference. We conclude the talk with insights on putting the theory into practice in graph analytics software.
Assoc. Prof. Lipo Wang,
Nanyang Technological University, Singapore
Dr. Lipo Wang received the Bachelor degree from National University of Defense Technology (China) and PhD from Louisiana State University (USA). His research interest is intelligent techniques with applications to optimization, communications, image/video processing, biomedical engineering, and data mining. He is (co-)author of 300 papers, of which 100 are in journals. He holds a U.S. patent in neural networks and a patent in systems. He has co-authored 2 monographs and (co-)edited 15 books. He was/will be keynote/panel speaker for 30 international conferences. He is/was Associate Editor/Editorial Board Member of 30 international journals, including 3 IEEE Transactions, and guest editor for 10 journal special issues. He was a member of the Board of Governors of the International Neural Network Society (for 2 terms), IEEE Computational Intelligence Society (CIS, for 2 terms), and the IEEE Biometrics Council. He served as CIS Vice President for Technical Activities and Chair of Emergent Technologies Technical Committee, as well as Chair of Education Committee of the IEEE Engineering in Medicine and Biology Society (EMBS). He was President of the Asia-Pacific Neural Network Assembly (APNNA) and received the APNNA Excellent Service Award. He was founding Chair of both the EMBS Singapore Chapter and CIS Singapore Chapter. He serves/served as chair/committee members of over 200 international conferences.
Data Analytics Using Intelligent Techniques Inspired from Nature
Abstract: This talk highlights some of our recent research results in data analytics using intelligent techniques inspired from nature. Our algorithms include compact radial-basis-function (RBF) neural networks, incrementally-generated fuzzy neural network, support vector machines with graded resolution, semi-exhaustive search and class-dependent feature selection algorithms. We demonstrate our algorithms in various challenging data analytics problems, such as chip fault detection, glaucoma diagnosis, EEG signal classification, stock trading and time series prediction, fighter jet response tactics, scanning acoustic microscope image analysis, action recognition in videos, content-based image retrieval, gene selection in microarray data, and face recognition.