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