Speakers 2022


Keynote Speaker Ⅰ


Prof. Dapeng David Zhang

IEEE Life Fellow, Fellow of RSC, IAPR and CAE, Distinguished Speaker of the IEEE Computer Society

The Chinese University of Hong Kong, China

Speech Title: Advanced Biometrics: Research and Development

Abstract: In recent times, an increasing, worldwide effort has been devoted to the development of automatic personal identification systems that can be effective in a wide variety of security contexts. As one of the most powerful and reliable means of personal authentication, biometrics has been an area of particular interest. It has led to the extensive study of biometric technologies and the development of numerous algorithms, applications, and systems, which could be defined as Advanced Biometrics.  This presentation will systematically explain this new research trend. As case studies, a new biometrics technology (palmprint recognition) and two new biometrics applications (medical biometrics and aesthetical biometrics) are introduced. Some useful achievements could be given to illustrate their effectiveness.

A short introduction to Prof. Dapeng David Zhang: 

Professor Zhang is X.Q. Deng Presidential Chair Professor of School of Data Science in the Chinese University of Hong Kong (Shenzhen), Director of Center for Computer Vision in Shenzhen Institute of Artificial Intelligence and Robotics for Society, Director of CUHK(SZ)-Linklogis Joint Laboratory of CV & AI. He is both Founder and Editor-in-Chief, International Journal of Image & Graphics (IJIG) (http://www.worldscinet.com/ijig/ijig.shtml) and Springer International Series on Biometrics (KISB) (http://www.springer.com/series/6191); Organizer, the first International Conference on Biometrics Authentication (ICBA); and Associate Editor of more than ten international journals including IEEE Transactions and so on. Over past 40 years, he have been working on pattern recognition, image processing and biometrics, where many research results have been awarded and some created directions, including palmprint recognition, computerized TCM and facial beauty analysis, are famous in the world. So far, he has published over 20 monographs, 500 international journal papers and 40 patents from USA/Japan/China. From 2014 to 2021, he has been continuously listed as a Highly Cited Researchers in Engineering by Clarivate Analytics (formerly known as Thomson Reuters) (http://highlycited.com). He is ranked 85 with H-Index 120 at Top 1,000 Scientists for international Computer Science and Electronics in 2021 (http://www.guide2research.com/scientists). Professor Zhang is also a Distinguished Speaker of the IEEE Computer Society, and an IEEE Life Fellow and an IAPR Fellow. Professor Zhang is selected as a Fellow of both the Royal Society of Canada and the Canadian Academy of Engineering.


Keynote Speaker Ⅱ


 Prof. Adil Baykasoğlu 

Dokuz Eylul University, Turkey

Speech Title: Dynamic Optimization through Intelligent Search Algorithms with Industrial Applications 

Abstract: It is hard to find an optimization problem with static parameters in real life industrial applications. Unpredictable machine failures, unplanned/urgent job arrivals, job cancellations, unexpected changes in production constraints, frequent changes in production lot sizes, changes in delivery times, several changes in short-mid-term plans such as job-work assignment, production planning are some well-known repeatedly encountered dynamic events in actual production systems. One of the main characteristic of these dynamic events is that, they cannot be known precisely beforehand and cannot be predicted easily. The problem domains that change over time (time-varying) or change via some unexpected events (event-based) or time-varying variants are usually called as dynamic environments and the problems with these characteristics are called to as dynamic optimization problems (DOPs). In this invited talk, several real life industrial applications of DOPs from our previous studies will be presented. These include; dynamic scheduling of heat treatment furnaces, dynamic part family formation for cellular manufacturing, dynamic scheduling of flexible manufacturing systems with flexible transportation abilities, dynamic load consolation for transportation operations, dynamic optimization of several CNC operations etc.

Brief Introduction to Prof. Adil Baykasoğlu:  Prof. Dr. Adil Baykasoglu received his B.Sc., M.Sc. and Ph.D. degrees from Mechanical and Industrial Engineering areas in Turkey (Gaziantep) and England (Nottingham). He is presently a full Professor and chair at the Industrial Engineering Department at the Dokuz Eylul University. He has published numerous academic papers, and 3 books and edited several conference books on operational research, computational intelligence, engineering management, and manufacturing systems design. He is also an active editor and editorial board member for many scientific journals. More details can be seen from his web pages: http://web.deu.edu.tr/baykasoglu 


Keynote Speaker Ⅲ


Prof. Chengzhong Xu

IEEE Fellow, Chief Scientist of Shenzhen Institutes of Advanced Technology

University of Macau, China

Speech Title: Connected and Autonomous Driving: Challenges and Opportunities

Abstract: Autonomous driving is breaking the dawn of a new era, mainly due to breakthroughs of AI technologies. This talk will provide a comprehensive review of state-of-the-art technologies in environment perception, scenario understanding, mapping and location, intelligent path planning. It will also introduce a MoCAD project for Macau Connected and Autonomous Driving, which is under development at University of Macau in collaboration with Chinese Academy of Sciences and Baidu Co. It aims to develop key enabling technologies in open environments with assistance of vehicle-infrastructure networking and cloud/edge computing technologies, and to construct a first-class test and evaluation platform for autonomous driving in the greater bay area. It will present recent research results on robustness deep machine learning algorithms in open environments and transfer learning approaches for model adaptivity in corner driving scenarios.  Model compression and acceleration techniques for the inference and cloud/edge systems support for autonomous driving will also be discussed.  

A brief introduction to Prof. Chengzhong Xu:

Dr. Cheng-Zhong Xu, IEEE Fellow, is the Dean of the Faculty of Science and Technology, University of Macau, Macao SAR, China, and a Chair Professor of Computer Science of UM. He is also a Chief Scientist of a key project on "Internet of Things for SmartCity" of the Ministry of Science and Technology of China and a key project on “Intelligent Driving” of Macau SAR. He was a Chief Scientist of Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences and the Director of the Institute of Advanced Computing and Digital Engineering. Prior to these, he was in the faculty of Wayne State University, the USA for 18 years. Dr. Xu's research interest is mainly in the areas of parallel and distributed systems, cloud and edge computing, and data-driven intelligent applications. He has published over 400 peer-reviewed papers on these topics and awarded more than 120 patents. Dr. Xu was the Chair of IEEE Technical Committee of Distributed Processing. He received his B.S. and M.S. degrees in Computer Science from Nanjing University and his Ph.D. from the University of Hong Kong in 1993.


Keynote Speaker Ⅳ


Professor Witold Pedrycz

IEEE Life Fellow, Professor and Canada Research Chair in Computational Intelligence

The University of Alberta, Canada

Speech Title: New Frontiers of Machine Learning: Federated Learning 

Abstract: With the rapid progress encountered in system modeling and simulation, especially in complex and human-centric systems, we have been witnessing important challenges. The visible requirements are inherently associated with the data and the way they are addressed in system modeling. We identify three ongoing challenges with far-reaching methodological implications, namely (i)modeling in the presence of strict constraints of privacy and security, (ii) efficient model building with limited data of varying quality, and (iii) knowledge distillation. We advocate that to conveniently address these challenges, it becomes beneficial to engage the fundamental framework of Granular Computing to enhance the existing approaches (such as e.g., federated learning in case of (i) and transfer knowledge in (ii)) or establish new directions to the problems. It is demonstrated that various ways of conceptualization of information granules as fuzzy sets, sets, rough sets, and others may lead to efficient solutions. To establish a sound conceptual modeling framework, we include a brief discussion of concepts of information granules and Granular Computing. In the sequel, a concise information granules-oriented design of rule-based architectures is discussed. A way of forming the rules through unsupervised federated learning is investigated along with algorithmic developments. A granular characterization of the model formed by the server vis-a-vis data located at individual clients is presented. It is demonstrated that the quality of the rules at the client’s end is described in terms of granular parameters and subsequently the global model becomes represented as a granular model with parameters in the form of information granules of type-2. The roles of granular augmentations of models in the setting of logic-oriented knowledge distillation are discussed.

A brief introduction to Professor Witold Pedrycz: 

Witold Pedrycz (IEEE Life Fellow) is a Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including the Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society. His main research directions involve Computational Intelligence, Granular Computing, knowledge discovery, data science, and knowledge-based neural networks among others. Dr. Pedrycz is involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer).  


Keynote Speaker Ⅴ


Dr. Shoujin Wang

Data Science at University of Technology Sydney

Speech Title: Recommender Systems: from Accuracy to Trustworthiness

Abstract: This talk will provide an introduction to recommender systems (RSs), one of the most widely used AI techniques in the real world.  Particularly, it will emphasize trustworthy (e.g., robust, fair, transparent, secure) RSs, the next-generation recommender systems which go beyond the traditional accuracy-oriented recommender systems. First, I will briefly introduce the background of recommender systems by answering three key questions, namely, What is an RS, Why we need RSs and How to build an RS? Then, I will introduce the classic methodologies for building RSs including collaborative filtering and content-based filtering, as well as the evaluation methods for RSs. Finally, I will illustrate the key concepts, main challenges together with methodologies in trustworthy RSs with some new insights. I will also share some future directions in this vibrant area. 

A brief introduction to Dr. Shoujin Wang: 

Shoujin Wang is a Lecturer in Data Science at University of Technology Sydney. He was a Research Fellow in Data Science at RMIT University from 2021 to 2022. Prior to joining RMIT, Shoujin was a postdoc at Macquarie University from 2018 to 2021. Shoujin obtained his PhD in data science from University of Technology Sydney in 2018. His main research interests include data mining, machine learning and recommender systems.  He has published more than 30 research papers in these areas, most of which were published at premier data science and AI conferences or journals, like The ACM Web Conference, AAAI, IJCAI and ACM Computing Surveys (CSUR). Shoujin has generally served as a PC member or a senior PC member at over 10 premier international data science conferences including KDD, AAAI, IJCAI, WSDM, CIKM and a reviewer for more than 10 prestigious journals including Machine Learning, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, etc. Shoujin is the recipient of various awards, including 2021 DAAD AINet Fellowships, 2022 Club Melbourne Fellowships. 



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