Speakers    

kEYNOTE SPEAKER
 

Prof. Dongrui Wu
(伍冬睿教授)
华中科技大学人工智能与自动化学院教授、博导、院长助理，图像信息处理与智能控制教育部重点实验室副主任，IEEE Fellow，IEEE模糊系统汇刊(IF=11.9)主编，《国家科学评论》信息学科编辑工作组成员
IEEE fellow

Huazhong University of Science and Technology, China

Topic: Machine Learning in Brain-Computer Interfaces

Bio-sketch: Dongrui Wu (IEEE Fellow) received a PhD in Electrical Engineering from the University of Southern California, Los Angeles, CA, in 2009. He is now Professor at 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 (13000+ Google Scholar citations; h=60). He received the IEEE Computational Intelligence Society Outstanding PhD Dissertation Award in 2012, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award in 2014, the IEEE Systems, Man and Cybernetics 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 (CAA) Early Career Award in 2021, the Ministry of Education Young Scientist Award in 2022, and First Prize of the CAA Natural Science Award in 2023. His team won National Champion of the China Brain-Computer Interface Competition in two successive years (2021-2022). Prof. Wu is the Editor-in-Chief of IEEE Transactions on Fuzzy Systems.

Speech Topic: Machine Learning in Brain-Computer Interfaces
Abstract: A brain-computer interface (BCI) enables direct communication between the brain and external devices. 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 decoding. Additionally, adversarial security and privacy protection are also very important to the broad applications of BCIs. This talk will introduce machine learning algorithms for accurate, secure and privacy-preserving BCIs.

 

Prof. Chao SheN
(沈超教授)

国家级领军人才特聘教授
国家优青
达摩院青橙奖
MIT TR35 China
 

Faculty of Electronic and Information Engineering

Xi'an Jiaotong University, CHINA

Bio-sketch: Chao Shen (Senior Member, IEEE) received the BS degree in automatic control and the PhD degree in system engineering from Xi’an Jiaotong University, Xi’an, China, in 2007 and 2014, respectively. He is currently a professor with the faculty of Electronic and Information Engineering, Xi’an Jiaotong University of China. From 2011 to 2013, he was a Joint PhD Student with Machine Learning of Carnegie Mellon University. And he also became the sole recipient of the 2023 the IEEE SMC Early Career Award for his significant contributions to theory and industrial applications of intelligent system control and security by the IEEE Systems, Man, and Cybernetics Society (IEEE SMC) on Oct 3. His research interests mainly include deep learning, data mining, AI security, and their applications for vision, big data, system security, and smart city. He is currently an associate editor for a number of journals, including the IEEE Transactions on Dependable Secure Computing and Journal of Franklin Institute, and TPC of conferences, including ACM CCS, NDSS, and ICDCS.

Speech Topic: Data-Driven Security Analysis of Machine Learning Systems
Abstract: Human society is witnessing a wave of machine learning (ML) driven by deep learning techniques, bringing a technological revolution for human production and life. In some specific fields, ML has achieved or even surpassed human-level performance. However, most previous machine learning theories have not considered the open and even adversarial environments, and the security and privacy issues are gradually rising. Besides of insecure code implementations, biased models, adversarial examples, sensor spoofing can also lead to security risks, which are hard to be discovered by traditional security analysis tools. This talk reviews previous works on ML system security and privacy, revealing potential security and privacy risks. Firstly, we introduce a threat model of ML systems, including attack surfaces, attack capabilities and attack goals. Second, we analyze security risks and countermeasures in terms of four critical components in ML systems: data input (sensor), data preprocessing, machine learning model and output. Finally, we discuss future research trends on the security of ML systems. The aim is to arise the attention of the computer security society and the ML society on security and privacy of ML systems, and so that they can work together to unlock ML’s potential to build a bright future.

 

Prof. Haizhou Li
(李海洲教授)

IEEE会士, ISCA会士

香港中文大学（深圳）数据科学学院校长
 

IEEE Fellow,  ISCA Fellow

The Chinese University of Hong Kong, Shenzhen

Bio-sketch: Haizhou Li is a Presidential Chair Professor and Executive Dean at the School of Data Science, The Chinese University of Hong Kong, Shenzhen, China. Prof. Li has taught in Nanyang Technological University in Singapore (2006-2016), National University of Singapore (2016-2021). Professor Li’' s research interests include automatic speech recognition and natural language processing. He served as the Editor-in-Chief of IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH AND LAN-GUAGE PROCESSING (2015-2018), the President of the international Speech Communication Association (2015-2017), the President of Asia Pacific Signal and information Processing Association (2015-2016), the President of the Asian Federation of Natural Language Processing (2017-2018), the Vice President of lEEE Signal Processing Society (2024-2026). He was the General Chair of ACL 2012, INTERSPEECH 2014, and IEEE ICASSP 2022.
Professor Li was the recipient of National lnfocomm Awards 2002, institution of Engineers Singapore (lES) Prestigious Engineering Achievement Award 2013 and 2015, President’s Technology Award 2013, and MTl Innovation Activist Gold Award 2015 in Singapore. He was named one of the two Nokia Visiting Professors in 2009 by Nokia Foundation, lEEE Fellow in 2014 for leadership in multilingual, speaker and language recognition, ISCA Fellow in 2018 for contributions to multilingual speech information processing, Bremen Excellence Chair Professor in 2019, and Fellow of the Academy of Engineering Singapore in 2022.

Speech Topic: A Computational Perspective to Language and Intelligence
Abstract：Natural language processing is part of artificial intelligence. In the era of large language models, let's review the development of natural language processing from machine translation to large language models in the history, draw on our understanding of human intelligence, and discuss the potential and limitations of large language models today.

 

 

Prof. Giancarlo Succi
 

Department of Computer Science and Engineering

University of Bologna, Italy

Bio-sketch: Giancarlo Succi (Member, IEEE) is a Professor with the Alma Mater Studiorum — University of Bologna, Italy. Before joining the University of Bologna, he was a Full Professor with Innopolis University, Russia; a Professor (tenure) with the Free University of Bolzano–Bozen, Italy, and the University of Alberta, Edmonton, AB, Canada; an Associate Professor with the University of Calgary, AB; and an Assistant Professor with the University of Trento, Italy. His research interests include multiple areas of software engineering, including open source development, agile methodologies, experimental software engineering, software engineering over the internet, software product lines, and software reuse.

Speech Topic: Pair Programming – (A)I in Action?
Abstract: This keynote explores the relationship between intelligence theories and pair programming. It reviews historical views on intelligence, from the Binet-Simon tests to Howard Gardner's Theory of Multiple Intelligences, which frames intelligence as a multidimensional construct. These ideas are strictly connected to pair programming, where two programmers collaborate, often with one guiding the other, reflecting Vygotsky’s Zone of Proximal Development (ZPD), where cognitive growth occurs through mentorship and scaffolding. Pair programming also exemplifies distributed cognition, where problem-solving and intelligence are shared across individuals and tools. By distributing cognitive tasks, both programmers enhance their skills and adapt to challenges more effectively. Altogether, Pair Programming evidences how intelligence is not an isolated trait but can be expanded through collaborations with people and using suitable (artificially) intelligent tools via shared cognitive processes.
 

 

Invited SPEAKERS
 

Assoc. Prof. Pavel Loskot
 

ZJU-UIUC Institute, Zhejiang University, China

Bio-sketch: Pavel Loskot joined the ZJU-UIUC Institute in January 2021 as an Associate Professor after being 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. In the past 25 years, he was involved in numerous collaborative research and development projects, and also held a number of paid consultancy contracts with industry. 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 interests focus on mathematical and probabilistic modeling, statistical signal processing and classical machine learning for multi-sensor data.

Prof. Dr. Abdel-Badeeh Mohamed Salem

Professor of Computer Science, Faculty of Computer and Information Sciences,
Ain Shams University, Cairo, Egypt
Founder &Head of Artificial Intelligence and Knowledge Engineering Research 

Bio-sketch: Prof. Dr. Abdel-Badeh M. Salem is a professor emeritus of Computer Science since September 2007 till now. He was a former Vice Dean of the Faculty of Computer and Information Sciences at Ain Shams University, Cairo-Egypt (1996-2007). He was a full professor Dr.of Computer Science at Faculty of Science, Ain Shams University from 1989 to 1996. He was a Director of Scientific Computing Center at Ain Shams University (1984-1990).In 1996 he was moved to the Faculty of Computer and Information Sciences at the same university. His research includes Knowledge engineering and computing, expert systems, intelligent medical and health informatics, and intelligent e-learning technologies. He has published around 850 papers in refereed journals and conference proceedings in these areas, 150 of them are in Scopus . He has been involved in more than 800 conferences and workshops as a Keynote speaker, member of Int. Program Committees, organizer and Session Chair. He is author and co-author of 25 Books in English and Arabic Languages.

Assoc. Prof. Mehdi Gheisari
 

Iran Shenzhen BKD Co.LTD, China; Islamic Azad University, Iran

Bio-sketch: Mehdi Gheisari (mehdi.gheisari61@gmail.com) is an Iranian Ph.D. holder in computer science who obtained his doctorate from China. He has actively engaged in collaborative projects with colleagues from various countries, spanning different domains, to expand his breadth of knowledge. His research interests encompass a wide range of areas, including IoT, E-Healthcare, Smart City, Machine Learning, Remote Sensing Data, Distributed Systems, and Cybersecurity.
In addition to his research pursuits, he has actively contributed to the academic community. He has served as a reviewer for well-established venues such as IEEE Communication Magazine and has been a member of the Technical Program Committee (TPC) for several conferences. Furthermore, he has taken on roles as an associate editor or guest editorial member for esteemed publications, including IEEE JSTAR, Cryptography, and JHCE.
For more information about Mehdi Gheisari's profile and publications, please visit his Google Scholar profile at:
https://scholar.google.com/citations?hl=en&user=tmWQt9UAAAAJ&view_op=list_works&sortby=pubdate