Keynote Speakers 专家报告

Prof. Wei Lu
University of Michigan, USA
Fellow of ASME

Wei Lu is a Full Professor at the Department of Mechanical Engineering, University of Michigan-Ann Arbor. He received his B.S. from Tsinghua University and a Ph.D. from Princeton University. Prof. Lu uses machine learning to address major challenges in energy and other applications. He has more than 180 journal publications in high impact peer-reviewed journals and 200 presentations and invited talks in international conferences, universities and national labs including Harvard, MIT and Stanford. He also has plenty of publications in conference proceedings, encyclopedias and book chapters. Prof. Lu was the recipient of many awards including the CAREER award by the US National Science Foundation; the Robert J. McGrattan Award by the American Society of Mechanical Engineers; Elected Fellow of the American Society of Mechanical Engineers; Robert M. Caddell Memorial Research Achievement Award; Faculty Recognition Award; Department Achievement Award; Novelis/CoE Distinguished Professor Award; CoE Ted Kennedy Family Faculty Team Excellence Award; CoE Creative, Innovative, Daring Award; CoE George J. Huebner, Jr. Research Excellence Award; and the Gustus L Larson Memorial Award by American Society of Mechanical Engineers. He was recognized as academics in the top 2% in the discipline of energy (a study from Stanford University science-wide author databases of standardized citation indicators and top 2% is the highest in the study). He was invited to the National Academies Keck Futures Initiative Conference multiple times.

Title:Integrating Machine Learning with Human Knowledge
Abstract: Machine learning has been heavily researched and widely used in many disciplines. However, achieving high accuracy requires a large amount of data that is sometimes difficult, expensive, or impractical to obtain. Integrating human knowledge into machine learning can significantly reduce data requirement, increase reliability and robustness of machine learning, and build explainable machine learning systems. This allows leveraging the vast amount of human knowledge and capability of machine learning to achieve functions and performance not available before and will facilitate the interaction between human beings and machine learning systems, making machine learning decisions understandable to humans. In this talk I will present some of our work in these areas, including self-directed online machine learning for topology optimization [1], which reduced the computational time by 5 orders of magnitude and outperformed all state-of-the-art algorithms tested, enabling design optimizations not possible before, integrating machine learning with human knowledge [2], machine learning toward advanced energy storage devices and systems [3], and application of machine learning for medical applications.

1. C. Deng, Y. Wang, C. Qin, Y. Fu, and W. Lu, “Self-directed online machine learning for topology optimization,” Nature Communications, 13, 388, 2022.
2. C. Deng, X. Ji, C. Rainey, J. Zhang, and W. Lu, “Integrating machine learning with human knowledge,” iScience, 23, 101656, 2020.
3. T. Gao and W. Lu, “Machine learning toward advanced energy storage devices and systems,” iScience, 24, 101936, 2021.

Prof. Dazi Li
Beijing University of Chemical Technology, China

Li Dazi is a Full Professor and the Vice Dean of the College of Information Science and Technology, Beijing University of Chemical Technology. She went to Japan to study abroad in 2000 and received the Ph.D. degree in engineering from the Department of Electrical and Electronic Systems, Kyushu University, Fukuoka, Japan, in 2004. She is also the head of the national first-class undergraduate program "Automation" and a renowned teaching teacher in Beijing. Her research interests include machine learning and artificial intelligence, advanced process control, fault diagnosis, complex system modeling and optimization. She is currently an Associate Editor of ISA Transactions.

Title: Graph Network based Deep Reinforcement Learning Methods for Complex System

ABSTRACT: Nowadays,
the process industries are becoming more and more complex, which brings challenges for data-driven real time intelligent decision-making. Reinforcement learning based method incorporating both prior knowledge and data-driven method is explored. Starting from graph representation of complex process, knowledge expression & extraction technology is studied based on reinforcement learning and graph neural network for application in industries field. The constructed knowledge automation system can organically integrate industrial data and expert experience, and realize the domain knowledge extraction and reconstruction for complex process industry driven by both prior knowledge and data. The proposed approaches for these problems can not only contribute to the theoretical support for deep graph reinforcement learning and its applications, but also provide an effective new solution for the intelligent transformation and applications in other domains under the goals of energy-saving and efficiency-enhancing.


Prof. Wei Fang,
Jiangnan University, China

Wei Fang is a Professor at the Department of Artificial Intelligence and Computer Science, Jiangnan University. His main research interests include swarm intelligence and evolutionary computing. In recent years, he has worked on the Neural Architecture Search (NAS), Pattern Mining, and Bayesian Network Structure Learning (BNSL) based on Evolutionary Algorithms. He has published more than 60 top journal and conference papers, including IEEE TPAMI、TEVC、TKDE、TCYB、CIM、TSMC、TII、Information Fusion、Information Sciences、KBS、ICDE、GECCO, etc. His Google Scholar citations is 3909 and h-index is 33. He was the principal investigator of projects granted from the National Natural Science Foundation of China, National Natural Science Foundation of Jiangsu Province, China Postdoctoral Science Foundation, etc. He is the editorial member of International Journal of Swarm Intelligence Research, International Journal of Computing Science and Mathematics, and Complex System Modeling and Simulation.

Title: Bayesian Network Structure Learning from Data based on Evolutionary Algorithm with Mutual Information and Structural Information
Abstract: Bayesian Network (BN) is a probability graph model that combines graph and probability theory and can express the causal relationship between variables clearly. It has played an essential role in representing and reasoning uncertain relationships, such as artificial intelligence, medical treatment, fault diagnosis, data mining, and other fields. BN learning has two major components: structure learning and parameter learning, in which structure learning is the basis of parameter learning and the premise of the BN application. BN structure learning (BNSL) from data has great significance and is also the main research direction of this paper. Due to the search space growing super-exponentially with the increasing number of nodes, BNSL has been proved to be an NPhard problem. At the same time, evolutionary algorithms are a promising way to solve such problems. Genetic algorithm (GA) has achieved many satisfying results these years but still faces the problems of low search efficiency, low accuracy, and insufficient integration with the problem. In this talk, the BNSL algorithm based on GA is studied. First, the search behaviour of GA is improved by using BN structural information. Besides, the superior information in the population is mined by mutual information and population support. It is used to guide GA to converge rapidly.




Keynote speakers in CSAI2022

Prof. Tao Xie
Peking University, China
Foreign Member of Academia Europaea
Fellow of ACM, Fellow of IEEE, Fellow of AAAS, Fellow of CCF
Peking University Chair Professor

Tao Xie is a Peking University Chair Professor, a Deputy Director of the Key Lab of High Confidence Software Technologies (PKU), Ministry of Education, and the Deputy Secretary General of the Emerging Engineering Development Committee of Peking University. He was a Full Professor at the Department of Computer Science, the University of Illinois at Urbana-Champaign (UIUC), USA. He is a Foreign Member of Academia Europaea, a Fellow of ACM, IEEE, AAAS, and CCF. He won an Xplorer Prize, NSFC Overseas Distinguished Young Scholar Award and its Extension Category, NSF Faculty CAREER Award, ACM SIGSOFT Distinguished Service Award, IEEE TCSE Distinguished Service Award, MSR Foundational Contribution Award, TSE 2018 Best Paper Award, ASE 2021 Most Influential Paper Award, etc. He serves as a Deputy Director of CCF TCSE, Chair of CCF-IEEE CS Young Scientist Award Committee, Program Chair of China National Computer Congress (CNCC 2020), Program Co-Chair of ICSE 2021, Co-Editor-in-Chief of Wiley Journal of Software Testing, Verification and Reliability (STVR), etc. His main research interests include software engineering, system software, software security, trustworthy AI.

Prof.Ninghui Li
Purdue University, USA
Fellow of ACM, Fellow of IEEE

Ninghui Li is a Samuel D. Conte Professor of Computer Science at Purdue University. He received a Bachelor's degree from the University of Science and Technology of China (USTC) in 1993, and a Ph.D. in Computer Science from New York University in 2000. His research interests are in security and privacy, and he has published over 200 referred papers in these areas. His 2007 paper ``t-Closeness: Privacy Beyond k-Anonymity and l-Diversity'' received the ICDE 2017 Influential Paper award.

He is Editor-in-Chief for ACM Transactions On Privacy and Security since October 2020, and has served as Program Chair for several leading conferences in the field, including ACM CCS, ESORICS, ACM ASIACCS, ACM SACMAT, and IFIP TM. He served as Chair of ACM Special Interest Group on Security, Audit and Control (SIGSAC) from 2017 to 2021, and Vice Chair from 2013 to 2017. He is ACM Fellow and IEEE Fellow.

Prof. Li's research has been supported by multiple NSF grants, including an NSF CAREER award in 2005. His research has also been supported by Air Force Office of Scientific Research (AFOSR), Army Research Office (ARO), National Security Agency (NSA), IBM Research, Google, and Samsung. He collaborated with Dr. Bertino on NSF and AFOSR funded projects.

Keynote speakers in CSAI2021

Prof. Benjamin W. Wah
The Chinese University of Hong Kong, Hong Kong
Fellow of the American Association for the Advancement of Science

Fellow of 
IEEE; Fellow of the Association for Computing Machinery

Wah was born in Hong Kong and graduated from Queen Elizabeth School, Hong Kong. He received his BS and MS in Electrical Engineering and Computer Science from Columbia University, USA, then furthered his studies at the University of California, Berkeley, obtaining an MS in Computer Science and a PhD in Databases.[1] Wah began his teaching career in Purdue University in 1979. He later joined the University of Illinois, Urbana-Champaign, in 1985, which he served until his retirement at the end of 2011.

In 1985-2011, he was the Franklin W. Woeltge Endowed Professor of Electrical and Computer Engineering at the University of Illinois, Urbana-Champaign, USA. In 2008-2009, he also served as Director of the Advanced Digital Sciences Center in Singapore, a US$50 million research center established by the University of Illinois in Singapore in collaboration with the Singapore government's Agency for ScienceTechnology and Research. In 1998–1999, Wah was Chair Professor of Computer Science and Engineering at The Chinese University of Hong Kong (CUHK), and in that year received an Exemplary Teaching Award. From 1999 to 2003, he served as Adjunct Professor in the Department of Computer Science and Engineering at CUHK. Between 2009-2019, he served as Provost of the Chinese University of Hong Kong.

Wah is an expert on non-linear programmingmultimedia signal processing and artificial intelligence. He has published numerous research articles in top professional journals, such as Artificial Intelligence, IEEE Trans. in Computers, IEEE Trans. on Knowledge and Data Engineering, IEEE Trans. on Multimedia, IEEE Trans. on Parallel and Distributed Technology, IEEE Trans. on Software Engineering, Journal of Global Optimization, Journal of Artificial Intelligence Research. He is also the author of two books, and Editor-in-Chief of Wiley's Encyclopedia of Computer Science and Engineering (published in 2008), and has contributed to many edited books and book chapters. He has served on many journal editorial boards.

He also holds many Endowed Professorships and Honorary Professorships in leading universities in the United States of America and in Asia. Professor Wah was elected President of IEEE Computer Society in 2001. He was a member of the Research Grants Council of Hong Kong between 2005 and 2009 and Chairman of its Engineering Panel between 2006 and 2009. He has been a member of the HK Research Grants Council since 2011.

Professor Wah has received numerous honors and awards for his distinguished academic and professional achievements, including the Tsutomu Kanai Award, the W. Wallace McDowell Award, and the Richard E. Merwin Distinguished Service Award, all from the IEEE Computer Society, the Pan Wen Yuan Foundation Outstanding Research Award, and the IEEE Third Millennium Medal. In 2011, he received the Distinguished Alumni Award in computer science from the University of California, Berkeley. He has been elected: Fellow of the American Association for the Advancement of Science, Fellow of IEEE, Fellow of the Association for Computing Machinery.


Prof. Xiaodong LI, IEEE Fellow,
RMIT University, Melbourne, Australia

Xiaodong Li (M’03-SM’07-Fellow’20) received his B.Sc. degree from Xidian University, Xi'an, China, and Ph.D. degree in information science from University of Otago, Dunedin, New Zealand, respectively. He is a Professor with the School of Science (Computer Science and Software Engineering), RMIT University, Melbourne, Australia. His research interests include machine learning, evolutionary computation, neural networks, data analytics, multiobjective optimization, multimodal optimization, and swarm intelligence. He serves as an Associate Editor of the IEEE Transactions on Evolutionary Computation, Swarm Intelligence (Springer), and International Journal of Swarm Intelligence Research. He is a founding member of IEEE CIS Task Force on Swarm Intelligence, a vice-chair of IEEE Task Force on Multi-modal Optimization, and a former chair of IEEE CIS Task Force on Large Scale Global Optimization. He is the recipient of 2013 ACM SIGEVO Impact Award and 2017 IEEE CIS "IEEE Transactions on Evolutionary Computation Outstanding Paper Award". He is an IEEE Fellow.