Prof. Witold Pedrycz, IEEE Life Fellow
Engineering - Electrical & Computer Engineering Dept, University of Alberta
Biography: Dr. Witold Pedrycz (IEEE Life Fellow) is 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 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, fuzzy modeling and Granular Computing, knowledge discovery, pattern recognition, data science, 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).
Title: Credibility of Machine Learning Architectures: Designing Self-Awareness Mechanisms
Abstract: Over the recent years, we have been witnessing spectacular and far-reaching achievements and applications of Artificial Intelligence and Machine Learning (ML), in particular. Efficient and systematic design of their architectures is important. Equally important are comprehensive evaluation mechanisms aimed at the assessment of the quality of the obtained results. The credibility of ML models is also of concern to any application, especially the one exhibiting a high level of criticality commonly encountered in autonomous systems. With this regard, there are a number of burning questions: how to quantify the quality of a result produced by the ML model? What is its credibility? How to equip the models with some self-awareness mechanism so careful guidance for additional supportive experimental evidence could be triggered? Proceeding with a conceptual and algorithmic pursuits, we advocate that these problems could be formalized in the settings of Granular Computing. We show that any numeric result be augmented by the associated information granules and the quality of the results is expressed in terms of the characteristics of information granules such as coverage and specificity. Different directions are covered including confidence/ prediction intervals, granular embedding of ML models, and granular Gaussian Process models. Several representative and direct applications in the realm of transfer learning, knowledge distillation, and federated learning are discussed.
Prof. Qinming Yang, IEEE Senior Member
Zhejiang University, China
Biography: Qinmin Yang received the Bachelor's degree in Electrical Engineering from Civil Aviation University of China, Tianjin, China in 2001, the Master of Science Degree in Control Science and Engineering from Institute of Automation, Chinese Academy of Sciences, Beijing, China in 2004, and the Ph.D. degree in Electrical Engineering from the University of Missouri-Rolla, MO USA, in 2007. From 2007 to 2008, he was a Post-doctoral Research Associate at University of Missouri-Rolla. From 2008 to 2009, he was a system engineer with Caterpillar Inc. From 2009 to 2010, he was a Post-doctoral Research Associate at University of Connecticut. Since 2010, he has been with the State Key Laboratory of Industrial Control Technology, the College of Control Science and Engineering, Zhejiang University, China, where he is currently a professor. He has also held visiting positions in University of Toronto and Lehigh University.
Title: Enhancing Wind Energy Harvesting by Industrial Data Intelligence
Abstract: Wind energy has been considered to be a promising alternative to current fossil-based energies. Large-scale wind turbines have been widely deployed to substantiate the renewable energy strategy of various countries. In this talk, challenges faced by academic and industrial communities for high reliable and efficient exploitation of wind energy are discussed. Industrial data intelligence is introduced to (partially) overcome problems, such as uncertainty, intermittence, and intense dynamics. Theoretical results and attempts for practice are both present.
Prof. Zhenming Yuan
Hangzhou Normal University, China
Biography: Zhenming Yuan is currently a Professor and PhD supervisor with the School of Information Science and Technology, Hangzhou Normal University, P.R. China. He is currently the deputy director of the Engineering Research Center of Mobile Health Management, Ministry of Education. He received his Ph.D. from the School of Computer Science and Technology of Zhejiang University in 2005, and was a visiting scholar at Columbia University and the New York Institute of Psychiatry. He served as the chairman of the supervision committee of the Hangzhou branch of the Chinese Computer Federation (CCF), the director of the Internet Health Management Branch of the China Health Management Association, and the standing director of the Zhejiang Provincial Maternal and Child Health Big Data Artificial Intelligence Special Committee. His research interests include medical artificial intelligence, intelligent multimedia technology, machine learning, information retrieval, etc.. He has served as a reviewer for journals such as Artificial Intelligence In Medicine, Expert Systems With Applications, and International Journal of Medical Informatics, and has published more than 100 papers in academic journals.
Title: Artificial intelligence methods for maternal and infant health
Abstract: Maternal and infant health is the cornerstone of national health, and digital treatment based on artificial intelligence provides a new intelligent evaluation model for pregnancy health and obstetric disease detection. The management and application of maternal and infant health based on artificial intelligence cover the entire pregnancy cycle. This talk will introduce some artificial intelligence methods to analyze and process a large amount of clinical data related to obstetrics, bringing strong potential for obstetric disease risk assessment, including low-cost obstetric disease screening in early pregnancy, dynamic monitoring of disease risk in mid pregnancy, and quality control of fetal abdominal ultrasound imaging and automatic measurement of subcutaneous tissue thickness in late pregnancy, in order to evaluate the development of newborns. This talk will analyze the current application status of artificial intelligence in the field of maternal and infant health, introduce the relevant research results of the team, and explore future work prospects.
Assoc.Prof. Pavel Loskot, IEEE Senior Member
Zhejiang University-University of Illinois at Urbana-Champaign Institute (ZJUI), China
Biography: Pavel Loskot joined the ZJU-UIUC Institute in January 2021 as the Associate Professor after being nearly 14 years with Swansea University in the UK. He received his PhD degree in Wireless Communications from the University of Alberta in Canada, and the MSc and BSc degrees in Radioelectronics and Biomedical Electronics, respectively, from the Czech Technical University of Prague in the Czech Republic. He is the Senior Member of the IEEE, Fellow of the Higher Education Academy in the UK, and the Recognized Research Supervisor of the UK Council for Graduate Education. His current research interest focuses on problems involving statistical signal processing and importing methods from Telecommunication Engineering and Computer Science to other disciplines in order to improve the efficiency and the information power of system modeling and analysis.
Title: Computing Tools and Methods for Predicting Protein Structures and Interactions
Abstract: The recent progress in mathematical modeling and computing methods brought algorithms also into biological sciences. At the molecular level, the most fundamental biological laws center on protein functions and interactions. Fortunately, the function in complex systems appear to be intimately connected with an underlying structure, which allows predicting the functions. In case of proteins, the long-chains of amino-acids residues are folded into complex multi-level 3D structures, which determines their function in biological systems. In this talk, I will review the basic principles of predicting the protein structures and interactions including physics-based molecular dynamic simulations, how these simulations have been enhanced with machine-learning and even mathematical topology methods, and what software are available for these tasks.