Keynote Speakers
l Yiyu Yao
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Xiping Hu Beijing Institute of Technology, China PhD & Professor, Fellow of the Singapore Academy of Engineering |
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Shiwen Mao Auburn University, Auburn, AL, USA PhD & Professor |
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Jindong Tan The University of Tennessee, Knoxville, USA PhD & Professor |
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Yiyu Yao University of Regina, Canada PhD & Professor, IRSS Fellow |
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F. Richard Yu Carleton University, Canada PhD & Professor, Fellow of the FRSC, CAE, EIC, IEEE, and IET |
AI-driven Complex Behaviors of Human Groups in Intelligent Agents
Xiping Hu, PhD & Professor
Fellow of the Singapore Academy of Engineering
Beijing Institute of Technology, China
Email: hxppp@163.com
Abstract: Intelligent agents, capable of perceiving their environments, making decisions, and acting, are profoundly shaping the future of key domains, including intelligent transportation, public safety, and automated manufacturing. However, within the predominantly data-driven, passive decision-making AI paradigm, current agents continue to face significant challenges, such as insufficient cognitive capabilities, limited active perception, and weak coordination. Therefore, this presentation focuses on “human-like cognitive mechanisms and complex group behaviors”, examining methods and mechanisms for endowing agents with human-like cognitive evolution and group interaction abilities in complex scenarios, thereby more effectively empowering a wide range of industries.
Bio-Sketch: Prof. Xiping Hu received his PhD from the University of British Columbia, Canada. He is a Fellow of the Singapore Academy of Engineering. He is currently a full professor and doctoral supervisor at the Beijing Institute of Technology, Executive Dean of the Artificial Intelligence Research Institute at Shenzhen MSU-BIT University, and Director of the Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing. His research covers swarm intelligence, cyber-physical systems, and affective computing, with recent work on multimodal user modeling and EEG- and behavior-based affective recommendation. He has published over 280 papers in venues including Nature sub-journals, Proceedings of the IEEE (cover article), IEEE TPAMI (cover article), AAAI (Oral), and ICML, and has been listed among Stanford University’s World’s Top 2% Scientists every year since 2019. As principal investigator, he has led research grants from agencies including NSFC, NSERC (Canada), the European Union, IBM, and TELUS. He has served as General Co-Chair of the 8th IEEE SmartIoT and the 16th IEEE CloudCom, and as an invited speaker at the University of Cambridge, IBM Watson Research, and the National University of Defense Technology.
Cloud Computing Meets Functional Data Analysis for Wireless and Network Intelligence
Shiwen Mao, PhD & Professor
Auburn University, Auburn, AL, USA
Email: smao@auburn.edu
Abstract: Cloud
computing underpins modern data infrastructure and continuously generates high
frequency telemetry from IoT sensors, serverless functions, and virtualized
resource logs. Similar time varying data streams arise across intelligent
transportation systems, wireless sensing platforms, and connected device
ecosystems. Such observations are inherently functional in nature, better
modeled as smooth trajectories evolving over continuous domains rather than as
isolated tabular records. Yet most analytics pipelines remain rooted in
discrete machine learning models that overlook temporal continuity, cross
trajectory dependence, and latent functional structure. Functional Data
Analysis (FDA) provides a principled statistical framework for modeling data at
the function level through smoothing, basis representations, and covariance
driven dimensionality reduction. Despite its strong theoretical foundations,
FDA remains underutilized in large scale computing and sensing systems due to
gaps between statistical methodology and engineering deployment. This talk
highlights FDA as a unifying modeling paradigm for time varying data,
presenting recent work in traffic flow modeling, RF sensing, RFID signal
analysis, and device fingerprinting. Across these domains, functional
representations demonstrate improved robustness to noise and missing data,
enhanced interpretability of temporal dynamics, and stronger cross domain
generalization, positioning FDA as a scalable foundation for modern cyber
physical analytics.
Bio-Sketch: Shiwen Mao is a Professor and Earle C. Williams Eminent Scholar and Director of the Wireless Engineering Research and Education Center at Auburn University, Auburn, AL, USA. Dr. Mao's research interest includes wireless networks, RF sensing and IoT, smart health, and machine learning. He is the editor-in-chief of IEEE Transactions on Cognitive Communications and Networking, an Associate Editor-in-Chief of IEEE Internet of Things Journal, a member-at-large on the Board of Governors of IEEE Communications Society, and Vice President of Technical Activities of IEEE Council on Radio Frequency Identification (CRFID). He was the General Chair of IEEE INFOCOM 2022, a TPC Chair of IEEE INFOCOM 2018, and a TPC Vice-Chair of IEEE GLOBECOM 2022. He is a co-recipient of several technical and service awards from the IEEE, and a Fellow of the IEEE in the Class of 2019.
AI-Driven Robotic Systems for the Future of Surgery
Jindong Tan, PhD & Professor
The University of Tennessee, Knoxville, USA
Email: tan@utk.edu
Abstract: Surgical
robotics is rapidly evolving through advances in artificial intelligence,
multimodal sensing, and embodied intelligence that enable robotic systems to
perceive, reason, and adapt in complex clinical environments. In this talk, I
present a unified vision for AI-driven robotic systems for the future of
surgery, where sensing, actuation, and intelligence are co-designed within a
closed-loop framework of Perception–Intelligence–Manipulation–Learning.
I will highlight our bio-inspired soft robotic technologies for maintaining
high-quality surgical vision, introduce an AI-powered surgical vision platform
that integrates multimodal sensing, real-time 3D reconstruction, and predictive
scene understanding, and discuss our secure federated platform for large-scale
medical imaging and clinical data sharing to accelerate AI development and
deployment. Finally, I will illustrate how these advances extend to collaborative
robotic manipulation and intelligent autonomy, outlining a future in which
AI-driven robotic systems enhance surgical precision, improve patient outcomes,
and transform healthcare.
Bio-Sketch: Jindong Tan received the Ph.D. degree from Michigan State University, East Lansing, MI, in 2002. He is currently a Professor and Associate Department Head of Biomedical Engineering at the University of Tennessee, Knoxville, and also holds joint appointments as a Professor of Mechanical and Aerospace Engineering and Professor of Surgery. His research focuses on embodied intelligence and intelligent robotic systems, innovating in both robotic platforms and foundational AI algorithms, with applications in surgical robotics, human-centered automation, and intelligent manufacturing. His work is supported by federal funding from NSF, DoD, NIH, and national laboratories, and he has published extensively in robotics and AI while contributing to the translation of research into practical technologies.
The Art, Science, and Applications of Three-way Decision
Yiyu Yao, PhD & Professor
IRSS Fellow University of Regina, Canada
Email: Yiyu.Yao@uregina.ca
Abstract: A theory of
three-way decision concerns thinking in threes, problem solving in threes, and
computing in threes. In this talk, I will cover the art, science, and
applications of three-way decision in three parts: (1) the foundations of
three-way decision, (2) the Dao of three-way decision, and (3) applications of
three-way in intelligent computing.
Bio-Sketch: Yiyu Yao is a professor of computer science with the Department of Computer Science, University of Regina, Canada. His research interests include three-way decision, granular computing, Web intelligence, rough sets, fuzzy sets, interval sets, formal concept analysis, information retrieval, machine learning, and data mining. He proposed a theory of three-way decision, a decision-theoretic rough set model, and a triarchic theory of granular computing. He has published over 400 papers. He was selected as a highly cited researcher by Clarivate from 2015 to 2019. He is the President of Web Intelligence Academy. He serves as a fellow of International Rough Set Society (IRSS). Personal homepage: http://www2.cs.uregina.ca/~yyao/.
Intelligence Networking for Agentic and Physical AI Systems
F. Richard Yu, PhD & Professor
Fellow of the CAE, IEEE, and IET
Carleton University, Canada
Email: richard_yu@carleton.ca
Abstract:
As AI systems become increasingly agentic — capable of autonomous
planning and action — and increasingly physical, embodied in connected and
autonomous vehicles, robots, and industrial agents, networks must coordinate
intelligence itself, not just data. Intelligence Networking treats intelligence
as a first-class networked resource, enabling predictive analytics, autonomous
decision-making, and adaptive physical systems that respond dynamically to
changing conditions. This talk examines the architecture and protocols needed
to realize this paradigm with different applications. The paradigm is grounded
theoretically in Intropy, a novel framework for modeling intelligence from the
presenter's recent book, which formalizes intelligence gain as information flow
moderated by resistance.
Bio-Sketch: F. Richard Yu obtained his PhD in Electrical Engineering from the University of British Columbia (UBC). His research interests include intelligent and autonomous systems, physical AI, IoT, and security/privacy. He has been consistently recognized as a Clarivate “Highly Cited Researcher,” Stanford “Top 2% Most Cited Scientist,” and ScholarGPS “Top 0.05% Highly Ranked Scholar” for several consecutive years. He has received several Best Paper Awards from first-tier conferences, two Carleton Research Achievement Awards (2012 and 2021), and the Ontario Early Researcher Award in 2011. He is a Member of the Academia Europaea (MAE) and a Fellow of the Royal Society of Canada (FRSC), Canadian Academy of Engineering (CAE), the Engineering Institute of Canada (EIC), IEEE, and IET. He also serves as a Board Member of the IEEE Vehicular Technology Society and as an IEEE Distinguished Speaker.