Biography: Dr. Yi Pan is currently a Chair Professor and the Dean of Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China and a Regents’ Professor Emeritus at Georgia State University, USA. He served as Chair of Computer Science Department at Georgia State University from 2005 to 2020. He has also served as an Interim Associate Dean and Chair of Biology Department during 2013-2017. Dr. Pan joined Georgia State University in 2000, was promoted to full professor in 2004, named a Distinguished University Professor in 2013 and designated a Regents' Professor (the highest recognition given to a faculty member by the University System of Georgia) in 2015.
Dr. Pan received his B.Eng. and M.Eng. degrees in computer engineering from Tsinghua University, China, in 1982 and 1984, respectively, and his Ph.D. degree in computer science from the University of Pittsburgh, USA, in 1991. His profile has been featured as a distinguished alumnus in both Tsinghua Alumni Newsletter and University of Pittsburgh CS Alumni Newsletter. Dr. Pan's current research interests mainly include bioinformatics and health informatics using big data analytics, cloud computing, and machine learning technologies. Dr. Pan has published more than 450 papers including over 250 journal papers with more than 100 papers published in IEEE/ACM Transactions/Journals. In addition, he has edited/authored 43 books. His work has been cited more than 18500 times based on Google Scholar and his current h-index is 88. Dr. Pan has served as an editor-in-chief or editorial board member for 20 journals including 7 IEEE Transactions. Currently, he is serving as an Editor-in-Chief of Big Data Mining and Analytics, an Associate Editor-in-Chief of Journal of Computer Science and Technology, Chinese Journal of Electronics, and IEEE/ACM Transactions on Computational Biology and Bioinformatics. He is the recipient of many awards including one IEEE Transactions Best Paper Award, five IEEE and other international conference or journal Best Paper Awards, 4 IBM Faculty Awards, 2 JSPS Senior Invitation Fellowships, IEEE BIBE Outstanding Achievement Award, IEEE Outstanding Leadership Award, NSF Research Opportunity Award, and AFOSR Summer Faculty Research Fellowship. He has organized numerous international conferences and delivered keynote speeches at over 70 international conferences around the world.
Dr. Pan is a Member of the Academy of the United Nations Sciences and Technology Organization, Foreign Member of Ukrainian Academy of Engineering Sciences, Fellow of American Institute for Medical and Biological Engineering, Fellow of Institute of Engineering Technology and Fellow of Royal Society for Public Health.
Biography: Professor Yun Li FIEEE taught at University of Glasgow for 28 years, where he was recognized as the second Top Author and served as the founding Director of University of Glasgow Singapore. Since 2021, he has been Director of Industrial Artificial Intelligence Centre at Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China. Inspired by AI, his early work resolved issues in PID control that had puzzled practising engineers for over 50 years, which since publication in IEEE Transactions on Control System Technology in 2005 has been its most popular article almost every month. He has published over 300 papers and books, and holds over 20 patents in China, Europe, United States and Japan. He has led or co-led over 30 research projects in the UK, EU, Singapore, and China, equivalent to over 20 million pounds in funding. Currently, his leads 2 major research projects funded by the National Natural Science Foundation of China for the next-generation AI models and by the National Key R&D Program of the Ministry of Science and Technology of China for the next-generation AI chips.
Title of Speech: Data and Knowledge Driven AI for Engineering and Technology Innovation
Abstract: The explosive growth in data volume and computational power has enabled the successful application of artificial intelligence (AI) to engineering science ("AI for Engineering"), greatly enhancing industrial innovation in terms of exploration, imagination, and creativity. This talk introduces, in conjunction with a generic large language model (LLM) based on the Artificial Neural Network (ANN) and the Kolmogorov-Arnold Network (KAN), the next generation of artificial intelligence that is explainable (XAI), driven by both data and knowledge. It illustrates the elevation of "Computer-Aided Design" (CAD) in the third paradigm of science to "Computer-Automated Design" (CAutoD) in the fourth paradigm, aiming at breaking through the intelligence limits of engineers, whereby enhancing innovation, strengthening competitiveness, and shortening development time. Applications will cover electronic design automation (EDA), engineering design, and control system modelling and design.
Biography: Dr. Yanbo Han has been a full professor in computer science since 2000 (first with the Institute of Computing Technology, Chinese Academy of Sciences, and now with the North China University of Technology). He holds a Ph.D. from the Technical University of Berlin. His research interests include Internet Computing, Stream Data Processing, Dependable Distributed Systems, and Business Process Management. He has authored or coauthored over 200 papers and 6 books. 12 of the acquired IPs have been transferred to the industry. Dr. Han has supervised 35 PhD theses. He has organized over 20 academic events as general chairs or program chairs, and has edited 14 journal special issues in the above-mentioned areas.
Title of Speech: Towards IoT-aware and Proactive BPM Systems
Abstract: Proactiveness is a raring feature of today’s BPM systems for coping with uncertainties. IoT enables BPM to perceive and react to real-time events in our physical world, and gives stimuli to promote process-level proactiveness. In designing IoT-aware and proactive BPM systems, however, people are often confronted with such issues as mismatch of the two established paradigms, and excessive complexity and inefficiency in dealing with temporal-spacial streams and decentralized computing. This talk elaborates some challenging issues in designing such proactive systems, and reports some intermediate progresses of an on-going project. Besides technical and methodological support, suitable architectural design is of importance to deal with the paradigm mismatch and decentralized computing. We thus also discuss our key architectural considerations and trade-offs in the second part of the talk.
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