Qingfu Zhang, 长江学者、IEEE Fellow
City University of Hong Kong, Hong Kong, China
Qingfu Zhang is a Chair Professor of Computational Intelligence 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. MOEA/D developed by his team has been one of the most widely used multiobjective optimization methodologies.
Professsor Zhang is an Associate Editor of the IEEE Transactions on Evolutionary Computation and the IEEE Transactions on Cybernetics. He is a Web of Science highly cited researcher in Computer Science for seven times from 2016. He is an IEEE fellow.
张青富，香港城市大学电脑科学系计算智能讲座教授、长江学者、IEEE Fellow。2016至2023连续入选Web of Science计算机科学领域的高被引学者，Google Scholar总引用超过38,000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。
(Onsite Talk) Speech Title: Modelling and Solution Set Constraints in MOEA/D
Most multi-objective evolutionary algorithms are to generate a finite set of solutions to approximate the Pareto optimal solution set (PS) in the decision space and the Pareto front (PF) in the objective space. In other words, these algorithms provide a zero-order approximation to the PS and PF. In this talk, I will first introduce basic ideas behind MOEA/D algorithms and explain how to use MOEA/D to learn a math model to approximate the PS and PF. In some engineering design areas, it is desirable or required that different optimal solutions should share some common patterns. Deb’s pioneering innovization methodology aim at addressing this requirement. I will explain our recently proposed concept of “solution set constraints” and show some of our preliminary work on using modelling in MOEA/D to handle these constraints. Our work can be regarded as a new attempt to do “innovization”, and it also provides a new way for defining optimality in multiobjective optimization. It can provide more useful information for supporting interactive multiobjective optimization.
Prof. Jixin Ma
University of Greenwich, UK
Dr Jixin Ma is a Full Professor of Computer Science in the School of Computing and Mathematical Sciences at University of Greenwich, U.K. He has been the Director of the Centre for Computer and Computational Science, and the Director of the School’s PhD/MPhil Programme. Professor Ma is also a Visiting Professor of Beijing Normal University, Hainan University, Auhui University, Zhengzhou Light Industrial University and Macau City University. Professor Ma obtained his BSc and MSc of Mathematics in 1982 and 1988, respectively, and PhD of Computer Sciences in 1994. His main research areas include Artificial Intelligence, Data Science and Information Systems, with special interests in Temporal Logic, Information Security, Machine Learning, Case-Based Reasoning and Pattern Recognition. Professor has been a member British Computer Society, American Association of Artificial Intelligence, ICIS/IEEE, World Scientific and Engineering Society, and Special Group of Artificial Intelligence of BCS. He has also been the Editor of several international journals and international conference proceedings, Conference/Program Chair, and Invited Keynote Speakers of many international conferences. Professor Ma has published more than 150 research papers in international journals and conferences.
(Onsite Talk) Speech Title: The Dividing Instant Puzzle - DIP
Abstract: The so-called Dividing Instant problem (DIP) is an ancient historical puzzle encountered when attempting to represent what happens at the boundary instant which divides two successive states. The specification of such a problem requires a thorough exploration of the primitives of the temporal ontology and the corresponding time structure, as well as the conditions that the resulting temporal models must satisfy. The problem is closely related to the question of how to characterise the relationship between time periods with positive duration and time instants with no duration. It involves the characterisation of the "closed" and "open" nature of time intervals, i.e., whether time intervals include their ending-points or not. In the domain of Artificial Intelligence, the DIP may be treated as an issue of how to represent different assumptions (or hypotheses) about the DIP in a consistent way. This talk examines various temporal models including those based solely on points, those based solely on intervals, and those based on both points and intervals, and points out the corresponding DIP with regard to each of these temporal models. A classification of assumptions about the DIP is introduced with a solution to the corresponding DIP.
Prof. Fei Teng
Southwest Jiaotong University, China
Teng Fei received her B.S. and M.S. degrees from the Southwest Jiaotong University, China, in 2006 and 2008, respectively. She received her Ph.D. at Ecole Centrale Paris in France in 2011. She serves as a professor of the School of Computer and Artificial Intelligence at Southwest Jiaotong University, China. Her research interests include medical data mining. She has published three monograph and over 100 research papers. Dr. Teng is the reviewer of the IEEE Journal of Biomedical and Health Informatics, Information Sciences, and IEEE Transactions on Computers. She is a director of the China Computer Federation(CCF), executive committee member of CCF Big Data and Service Computing Committee, deputy director of Pharmacy Inheritance and Development Committee of Sichuan Medical Communication Society, and executive director of Sichuan Higher Education Computer Basic Education Research Association. She has served as the chairman and member of the procedural committee for more than 10 academic conferences, including GreenCom, PAKDD, Bigdata, etc.
滕飞，教授，博士生导师，西南交通大学计算机与人工智能学院副院长。四川省杰出青年基金获得者、入选四川省“天府青城计划”青年人才。博士毕业于法国巴黎中央理工大学，美国旧金山州立大学和英国利兹大学访问学者。研究兴趣包括工业大数据、医疗大数据、服务计算等。在国内外顶级期刊和会议TC、TPDS、TCC、计算机学报、IJCAI等发表60余篇研究论文。主持国家自然科学基金、国家重点研发计划子课题、四川省科技计划项目等10余项国家省部级科研项目。担任计算机学报、Information Sciences等国内国际期刊的审稿人。现为CCF理事、CCF大数据、服务计算专委会执行委员、CCF YOCSEF成都主席（2020-2021），四川省医学传播学会药学传承与发展专委会副主任，四川省高等院校计算机基础教育研究会常务理事，担任GreenCom、PAKDD、Bigdata等10余个学术会议的程序委员会主席和委员。
(Onsite Talk) Speech Title: Automated International Classification of Diseases coding: development, challenges, and applications
Abstract: International classification of diseases coding (ICD coding) serves as a core task in clinical data management and plays a significant role in monitoring health issues, reporting diseases, collecting morbidity statistics, and assisting in medical reimbursement decision-making. This report will outline the importance of ICD coding and its role in modern medical information systems, with a focus on how data_based and knowledge-based methods utilize the complex characteristics within medical records to enhance the accuracy, stability, and interpretability of ICD coding. Additionally, the application and future directions of few-shot learning and zero-shot learning in disease coding and clinical text parsing will also be discussed.
Prof. Xianyong Li
Xihua University, China
Xianyong Li is a professor and master's supervisor at the School of Computer and Software Engineering of Xihua University in China. He received his doctoral degree from Chongqing University in China in 2014. He is the Sichuan Province High-level Overseas Talent, and Xihua University Young Scholars Reserve Talent. He is the members of the Network Space Search Committee of the Chinese Information Processing Society of China, the Affective Computing Committee, the China Computer Federation, and the Chinese Association for Artificial Intelligence. He is also the vice Chairman of the Natural Language Processing Professional Committee of the Sichuan Computer Federation. His research focuses on various problems of natural language processing, artificial intelligence, social network analysis, Evolution and guidance of network public opinion, sentiment analysis, etc. From September 2016 to September 2017, he worked as a visiting scholar at the State University of New York at Binghamton. He has presided over 9 projects, such as the National Natural Science Foundation Youth Fund, the Chunhui Program Project of the Ministry of Education, the Sichuan Science and Technology Program, and the Natural Science Foundation of Sichuan Education Department. He has published more than 40 academic papers in international main academic journals and conferences, and has authorized 2 national invention patents. He won two first prizes for Scientific and Technological Progress awarded by the China Federation of Logistics & Purchasing and the China Building Materials Circulation Association in 2023.
(Onsite Talk) Speech Title: Sentiment analysis methods with external knowledge
Abstract: External knowledge aims to complement the explicit sentiment clues of sentences, improving the sentiment analysis methods’ performance. Implicit sentiment word definitions and emojis always carry a lot of sentiment information. In this talk, we will introduce some sentiment analysis models with external knowledge including implicit sentiment word definitions and emojis. We will find that the sentiment analysis methods that are injected the external knowledge obtain better performance than other baselines. Extensive experiments show that the proposed sentiment analysis methods integrated the implicit sentiment word definitions, the emoji information and the relationships between texts and emojis are effective for sentiment analysis.
Shigang Chen, IEEE Fellow
University of Florida, America
Dr. Shigang Chen (sgchen@cise.ufl.edu) is a professor with Department of Computer and Information Science and Engineering at University of Florida. He received his B.S. degree in computer science from University of Science and Technology of China in 1993. He received M.S. and Ph.D. degrees in computer science from University of Illinois at Urbana-Champaign in 1996 and 1999, respectively. After graduation, he had worked with Cisco Systems for three years before joining University of Florida in 2002. His research interests include data streaming, Internet of things, cybersecurity, RFID technologies, intelligent cyber-transportation systems, etc. He published over 200 peer-reviewed journal/conference papers. He received the NSF CAREER Award and several best paper awards. He holds 13 US patents, and many of them were used in software products. He served as an associate editor for IEEE Transactions on Mobile Computing, IEEE/ACM Transactions on Networking and a number of other journals. He served in various chair positions or as committee members for numerous conferences. He held the University of Florida Research Foundation Professorship and the University of Florida Term Professorship. He is a Fellow of IEEE and an ACM Distringuished Scientist.