Prof. Witold Pedrycz
University of Alberta, Edmonton, Canada
IEEE Fellow
Speech Title: A Unified Framework of Data and Knowledge Environment of Machine Learning
Abstract: Over the recent years, we have been witnessing truly remarkable progress in Machine Learning (ML) with highly visible accomplishments encountered, in particular, in natural language processing and computer vision impacting numerous areas of human endeavours. Driven inherently by the technologically advanced learning and architectural developments, ML constructs are highly impactful coming with far reaching consequences; just to mention autonomous vehicles, control, health care imaging, decision-making in critical areas, among others.
Data are central and of paramount relevance to the design methodology and algorithms of ML. While they are behind successes of ML, there are also far-reaching challenges that require urgent attention especially with the growing importance of requirements of interpretability, transparency, credibility, stability, and explainability. As a new direction, data-knowledge ML concerns a prudent and orchestrated involvement of data and domain knowledge used holistically to realize learning mechanisms and support the formation of the models.
The objective of this talk is to identify the challenges and develop a unique and comprehensive setting of data-knowledge environment in the realization of the development of ML models. We review some existing directions including concepts arising under the name of physics informed ML. We investigate the representative topologies of ML models identifying data and knowledge functional modules and interactions among them. We also elaborate on the central role of information granularity in this area.
Short Bio: Witold Pedrycz (IEEE Life Fellow) is Professor 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 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, and Machine Learning, among others.
Professor Pedrycz serves as 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).

Prof. Shyi-Ming Chen
National Taiwan University of Science and Technology, Taiwan
IEEE Fellow, IET Fellow, IFSA Fellow, AAIA Fellow, IETI Distinguished Fellow, Fellow of the Pakistan Academy of Engineering
Short Bio: Shyi-Ming Chen is a Chair Professor in the Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He received the Ph.D. degree in Electrical Engineering from National Taiwan University, Taipei, Taiwan, in June 1991. He is an IEEE Fellow, an IET Fellow, an IFSA Fellow, an AAIA Fellow, an IETI Distinguished Fellow, and a Fellow of the Pakistan Academy of Engineering. He was the Dean of the College of Electrical Engineering and Computer Science, Jinwen University of Science and Technology, New Taipei City, Taiwan. He was the Vice President of the National Taichung University of Education, Taichung, Taiwan. He was the President of the Taiwanese Association for Artificial Intelligence (TAAI). He was the President of the Taiwanese Association for Consumer Electronics (TACE). He has published more than 600 papers in referred journals, conference proceedings and book chapters. His research interests include Fuzzy Systems, Intelligent Systems, Fuzzy Decision Making, Computational Intelligence, Knowledge-Based Systems, Machine Learning, Data Mining, Big Data Analysis, Genetic Algorithms, and Particle Swam Optimization Techniques. He is an Editor-in-Chief of Granular Computing, an Associate Editor of IEEE Transactions on Fuzzy Systems, an Associate Editor of IEEE Transactions on Cybernetics, an Associate Editor of IEEE Transactions on Artificial Intelligence, an Associate Editor of IEEE Access, an Associate Editor of Information Sciences, an Associate Editor of Knowledge-Based Systems, an Associate Editor of Applied Intelligence, an Editorial Board Member of Information Fusion, an Associate Editor of Journal of Intelligent & Fuzzy Systems, an Associate Editor of International Journal on Artificial Intelligence Tools, an Associate Editor of International Journal of Pattern Recognition and Artificial Intelligence, an Associate Editor of International Journal of Fuzzy Systems, an Associate Editor of Journal of Information Science and Engineering, an Associate Editor of Fuzzy Optimization and Decision Making, an Associate Editor of Knowledge and Information Systems, an Editor of International Journal of Intelligent Systems, an Editor of Mathematical Problems in Engineering, and an Editor of Engineering applications of Artificial Intelligence.

Prof. Zhongfei (Mark) Zhang
Binghamton University, State University of New York, USA
IEEE Fellow, IAPR Fellow, AAIA Fellow
Short Bio: Zhongfei (Mark) Zhang is a professor at Computer Science Department, Binghamton University, State University of New York (SUNY), USA. He received a B.S. in Electronics Engineering (with Honors), an M.S. in Information Sciences, both from Zhejiang University, China, and a PhD in Computer Science from the University of Massachusetts at Amherst, USA. His research interests are in the broad areas of machine learning, data mining, computer vision, and pattern recognition, and specifically focus on multimedia/multimodal data understanding and mining. He was on the faculty of Computer Science and Engineering at the University at Buffalo, SUNY, before he joined the faculty of Computer Science at Binghamton University, SUNY. He is the author or co-author of the very first monograph on multimedia data mining and the very first monograph on relational data clustering. He has published over 200 papers in the premier venues in his areas. He holds more than twenty inventions, has served as members of the organization committees of several premier international conferences in his areas including general co-chair and lead program chair, and as editorial board members for several international journals. He served as a French CNRS Chair Professor of Computer Science at the University of Lille 1 in France, a JSPS Fellow in Chuo University, Japan, a QiuShi Chair Professor in Zhejiang University, China, as well as visiting professorships from many universities and research labs in the world when he was on leave from Binghamton University years ago. He received many honors including SUNY Chancellor’s Award for Scholarship and Creative Activities, SUNY Chancellor’s Promising Inventor Award, and best paper awards from several premier conferences in his areas. He is a Fellow of IEEE, IAPR, and AAIA.

Prof. Yang Yue
Xi'an Jiaotong University, China
SPIE Fellow, IEEE Senior Member and Optica Senior Member
Short Bio: Yang Yue received the B.S. and M.S. degrees in electrical engineering and optics from Nankai University, China, in 2004 and 2007, respectively. He received the Ph.D. degree in electrical engineering from the University of Southern California, USA, in 2012. He is a Professor with the School of Information and Communications Engineering, Xi'an Jiaotong University, China. Dr. Yue’s current research interest is intelligent photonics, including optical communications, optical perception, and optical chip. He has published over 270 journal papers (including Science) and conference proceedings with >12,000 citations, six edited books, two book chapters, >60 issued or pending patents, >200 invited presentations (including 1 tutorial, >30 plenary and >60 keynote talks). Dr. Yue is a Fellow of SPIE, a Senior Member of IEEE and Optica. He is an Associate Editor for IEEE Access and Frontiers in Physics, Editor Board Member for four other scientific journals, Guest Editor for >10 journal special issues. He also served as Chair or Committee Member for >100 international conferences, Reviewer for >70 prestigious journals.

Prof. Yen-Wei Chen
Ritsumeikan University, Japan
Short Bio: Yen-Wei Chen received the B.E. degree in 1985 from Kobe Univ., Kobe, Japan, the M.E. degree in 1987, and the D.E. degree in 1990, both from Osaka Univ., Osaka, Japan. He was a research fellow with the Institute for Laser Technology, Osaka, from 1991 to 1994. From Oct. 1994 to Mar. 2004, he was an associate Professor and a professor with the Department of Electrical and Electronic Engineering, Univ. of the Ryukyus, Okinawa, Japan. He is currently a professor with the college of Information Science and Engineering, Ritsumeikan University, Japan. He is the founder and the first director of Center of Advanced ICT for Medicine and Healthcare, Ritsumeikan University. He was a chair professor with the college of computer technology and science, Zhejiang University, China during 2014-2016. His research interests include medical image analysis, computer vision and computational intelligence. He has published more than 300 research papers in a number of leading journals and leading conferences including IEEE Trans. Image Processing, IEEE Trans. Medical Imaging, CVPR, ICCV, MICCAI. He has received many distinguished awards including ICPR2012 Best Scientific Paper Award, 2014 JAMIT Best Paper Award. He is/was a leader of numerous national and industrial research projects.

Assoc. Prof. Yik-Chung Wu
The University of Hong Kong (HKU), China
IEEE Senior Member
Short Bio: Yik-Chung Wu received the B.Eng. (EEE) degree in 1998 and the M.Phil. degree in 2001 from the University of Hong Kong (HKU). He received the Croucher Foundation scholarship in 2002 to study Ph.D. degree at Texas A&M University, College Station, and graduated in 2005. From August 2005 to August 2006, he was with the Thomson Corporate Research, Princeton, NJ, as a Member of Technical Staff. Since September 2006, he has been with HKU, currently as an Associate Professor. He was a visiting scholar at Princeton University, in summers of 2015 and 2017. His research interests are in general areas of signal processing and communication systems , and in particular Bayesian inference, distributed algorithms, and large-scale optimization. Dr. Wu served as an Editor for IEEE Communications Letters, and IEEE Transactions on Communications. He is currently a Senior Area Editor for IEEE Transactions on Signal Processing, an Associate Editor for IEEE Wireless Communications Letters, and an Editor for Journal of Communications and Networks. He was a TPC member for over 100 IEEE major conferences. He received four best paper awards in international conferences, with the most recent one from IEEE International Conference on Communications (ICC) 2020. He is a senior member of the IEEE.

Assoc. Prof. Shijian Lu
Nanyang Technological University, Singapore
Speech Title: Artificial intelligence (AI) has achieved great progress with the advance of deep learning technology in the past few years. This trend recently even accelerates thanks to the development of foundation models in different research areas such as computer vision, natural language processing, etc. On the other hand, training deep network models especially large-scale foundation models usually requires a huge amount of training data and computational resources, where sustainability has become one big concern due to the high costs in data collection and deep network training. This talk will share our recent work that aims to tackle the sustainability of AI and deep learning from two complementary perspectives. The first is data synthesis, aims to generate realistic and high-fidelity data that can be directly and effectively applied for deep network training. We have explored various approaches such as image composition, image style transfer, image editing, etc., and obtained very promising outcomes. The second is knowledge transfer, aiming to leverage previously learnt knowledge or annotated data to facilitate the handling of new tasks with minimal data collection and network training. For this perspective, we have designed a series of techniques on unsupervised domain adaptation, unsupervised model adaptation, domain generalization, etc.
Short Bio: Prof Lu is an Associate Professor in the College of Computing and Data Science, Nanyang Technological University. He received his PhD in electrical and computer engineering from the National University of Singapore. His research interests include computer vision and deep learning. He has published more than 100 internationally refereed journal and conference papers and co-authored over 10 patents in these research areas. Before joining in NTU, he took several leadership roles in the Institute for Infocomm Research (I2R), the Agency for Science, Technology, and Research (A*SATR) in Singapore, including Head of Visual Attention Lab, Deputy Head of Satellite Department, etc. Dr Lu is currently an Associate Editor for the journals of Pattern Recognition (PR) and Neurocomputing. He has also served in the program committee of several international conferences such as the Area Chair of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Senior Program Committee of the International Joint Conferences on Artificial Intelligence (IJCAI) and AAAI Conference on Artificial Intelligence (AAAI), the General Chair of the IAPR International Workshop on Document Analysis System (DAS) in 2020, etc.