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Keynote Speakers

Prof. Dongrui Wu
IEEE Fellow

Huazhong University of Science and Technology, China

Bio.: Dongrui Wu (IEEE Fellow) received a B.E in Automatic Control from the University of Science and Technology of China, Hefei, China, in 2003, an M.Eng in Electrical and Computer Engineering from the National University of Singapore in 2006, and a PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2009. He is now Professor and Deputy Director of the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. 

Prof. Wu's research interests include brain-computer interface, machine learning, computational intelligence, and affective computing. He has more than 200 publications (11000+ Google Scholar citations; h=54). He received the IEEE Computational Intelligence Society (CIS) Outstanding PhD Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the IEEE Systems, Man and Cybernetics (SMC) Society Early Career Award in 2017, the USERN Prize in Formal Sciences in 2020, the IEEE Transactions on Neural Systems and Rehabilitation Engineering Best Paper Award in 2021, the Chinese Association of Automation Early Career Award in 2021, and the Ministry of Education Young Scientist Award in 2022. His team won the First Prize of the China Brain-Computer Interface Competition in four successive years (2019-2022). Prof. Wu is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.

Title: Machine Learning in Brain-Computer Interfaces

Abstract: A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. Electroencephalograms (EEGs) used in BCIs are weak, easily contaminated by interference and noise, non-stationary for the same subject, and varying across different subjects and sessions. Thus, sophisticated machine learning approaches are needed for accurate and reliable EEG-based BCIs. This talk will introduce the basic concepts of BCIs, review the latest progress, and describe several newly proposed machine learning approaches for BCIs.

Prof. Nian Zhang

University of the District of Columbia (UDC), USA

Bio.: Dr. Nian Zhang is a Professor in the Department of Electrical and Computer Engineering at the University of the District of Columbia (UDC), Washington, D.C., USA. She received her Ph.D. degree in Computer Engineering from Missouri University of Science & Technology, USA, and Master’s degree in Automatic Control from Huazhong University of Science and Technology, China. Her research interests include machine learning, deep learning, classification, clustering, and optimization. Dr. Zhang was awarded numerous federal grants from the National Science Foundation, Department of Defense, and National Institutes of Health as the PI/Co-PI, accumulating over $4.5 million in research grant funds. Dr. Zhang serves as an Associate Editor for the IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, Knowledge-Based Systems, and IEEE/CAA Journal of Automatica Sinica. She also serves on the Editorial Board of the Complex & Intelligent Systems. In addition, Dr. Zhang serves as the Chair of the IEEE Computational Intelligence Society (CIS) Task Force on "Interdisciplinary Emergent Technologies" and the Vice Chair of the IEEE CIS’ Adaptive Dynamic Programming and Reinforcement Learning Technical Committee. She regularly serves as the Program Chair and Publications Chair of annual international conferences, including ISNN, ICACI, ICICIP, and ICIST. Dr. Zhang received the IEEE Transactions on Neural Networks and Learning Systems (TNNLS) “Outstanding Associate Editor Award” in 2020. She also received the UDC’s faculty recognition awards for Excellence in Research Award, Excellence in Teaching Award, and Outstanding Undergraduate Research Mentorship Award in three consecutive years.

Title: Recent Advances and Trends on Hyperspectral Image Preprocessing, Optimization, and Classification Techniques

Abstract: Hyperspectral image classification is an important research topic that focuses on assigning class labels to pixels, which is very challenging because of unbounded size and imbalanced nature of data. This talk will introduce a new computational framework that integrates data preprocessing, optimization, and classification techniques to distinguish trace chemicals from the substrates on which they rest at standoff distances. This talk will first introduce a new data preprocessing method aimed at addressing the class imbalance and unlabeled data challenges commonly encountered in real-world hyperspectral images. Then, a creative approach for extracting endmembers through a global optimization algorithm will be presented. Finally, possible trends and new research directions will be discussed.

Dr. Yu Zheng

IEEE Fellow


Bio.: Dr. Yu Zheng is the Vice President of JD.COM and president JD Intelligent Cities Research. Before Joining JD.COM, he was a senior research manager at Microsoft Research. He was the Editor-in-Chief of ACM Transactions on Intelligent Systems and Technology and has served as the program co-chair of ICDE 2014 (Industrial Track), CIKM 2017 (Industrial Track) and IJCAI 2019 (industrial track). He is also a keynote speaker of AAAI 2019, KDD 2019 Plenary Keynote Panel and IJCAI 2019 Industrial Days. His monograph, entitled Urban Computing, has been used as the first text book in this field. He received the SIGKDD Test-of-Time Award in 2023 and SIGSPATIAL 10-Year-Impact Award for three times in 2019, 2020 and 2022 respectively. In 2013, he was named one of the Top Innovators under 35 by MIT Technology Review (TR35) and featured by Time Magazine for his research on urban computing. In 2016, Zheng was named an ACM Distinguished Scientist and elevated to an IEEE Fellow in 2020 for his contributions to spatio-temporal data mining and urban computing.

Title: Urban Computing: Building intelligent Cities using AI and Big Data

Abstract: Urban computing connects ubiquitous sensing technologies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that improve urban environment, life quality, and city operation systems. This talk presents the vision and framework of urban computing, introducing the challenges and the-state-of-the-art solutions in each layer of the framework. Based on the vision of urban computing, we have built an intelligent city operation system which has been deployed in over 20 cities as a digital foundation to empower Big Data-driven applications.