Keynote Speakers

Ordered List by Surname Alphabetical Order:

l Jin-Song Dong

l Haibin Ling

l Mohamad Sawan

l Jeannette M. Wing

l Jiantao Zhou

	Jin-Song Dong National University of Singapore PhD & Professor, Fellow of the Institute of Engineers Australia (EA)
	Haibin Ling Westlake University, Hangzhou, China PhD & Professor, Fellow of the Institute of Electrical and Electronic Engineers (IEEE)
	Mohamad Sawan Westlake University, Hangzhou, China PhD & Professor, Fellow of the Royal Society of Sciences of Canada (FRSC), a Fellow of the Canadian Academy of Engineering (FCAE), a Fellow of the Engineering Institutes of Canada (FEIC), a Life Fellow of the Institute of Electrical and Electronic Engineers (LFIEEE), and an “Officer” of the National Order of Quebec
	Jeannette M. Wing Columbia University in the City of New York, USA PhD & Professor., Member of the National Academy of Engineering (NAE) and the Massachusetts Institute of Technology (MIT) Corporation, Fellow of the American Academy of Arts and Science, American Association for the Advancement of Science, Association for Computing Machinery (ACM), Institute of Electrical and Electronic Engineers (IEEE), and National Academy of Innovators
	Jiantao Zhou University of Macau, China PhD & Professor

Trusted AI and Reasoning Beyond LLM

Jin-Song Dong, Dr. Prof., Fellow of the Institute of Engineers Australia (EA)

National University of Singapore

Email: dcsdjs@nus.edu.sg

https://www.comp.nus.edu.sg/~dongjs/

Abstract: Machine Learning (ML) systems have become increasingly integral to safety and security-critical applications. However, a significant challenge arises from the inherent lack of explainability and verifiability in many ML systems. Our recent research has focused on addressing this issue by developing a Trusted ML system. The initial segment of this presentation delves into the "Silas: Trusted Machine Learning System," an initiative that seamlessly integrates open machine learning with formal automated reasoning (www.depintel.com). In the subsequent part of the discussion, we explore the reasoning capabilities of LLM (encompassing ChatGPT3.5 and GPT4). Specifically, we discuss the approaches to link LLM with formal reasoning techniques, aiming to establish a framework for trusted LLM agents. As a practical demonstration, we will present the application of probabilistic reasoning, machine learning, LLM, and computer vision to sports analytics and share the vision of a new international sports analytics conference series (https://formal-analysis.com/isace/2025/).

Bio-Sketch: Dr. Jin-Song Dong is professor of computer science department at the National University of Singapore. His research spans a range of fields, including formal methods, safety and security systems, probabilistic reasoning, sports analytics, and trusted machine learning. He co-founded the commercialized PAT verification system, which has garnered thousands of registered users from over 150 countries and received the 20-Year ICFEM Most Influential System Award. Jin Song also co-founded the commercialized trusted machine learning system Silas (www.depintel.com). He has received numerous best paper awards, including the ACM SIGSOFT Distinguished Paper Award at ICSE 2020. He served on the editorial board of ACM Transactions on Software Engineering and Methodology, Formal Aspects of Computing, and Innovations in Systems and Software Engineering, A NASA Journal. He has successfully supervised 29 PhD students, many of whom have become tenured faculty members at leading universities worldwide. In his leisure time, Jin Song developed Markov Decision Process (MDP) models for tennis strategy analysis using PAT, assisting professional players with pre-match analysis (outperforming the world's best). He is also a Fellow of the Institute of Engineers Australia.

Visual Intelligence for Enhancing Optical Coherence Tomography Imagery

Haibin Ling, Dr. Prof., Fellow of the Institute of Electrical and Electronic Engineers (IEEE)

Westlake University, Hangzhou, China

Email: hling@cs.stonybrook.edu

https://www3.cs.stonybrook.edu/~hling/

Abstract: The rapid advancement of imaging techniques and artificial intelligence has revolutionized research and applications in visual intelligence (VI). In this talk, I will present our recent studies on improving Optical Coherence Tomography (OCT) imagery, a pivotal technology with extensive applications in both preclinical and clinical diagnoses. While recent advancements in machine learning have shown promising progress in OCT, current solutions still face significant challenges, such as (1) the absence of accurate ground truth data typically required for supervised training and (2) the difficulty of integrating nuanced yet informative raw signals. In this talk, I will introduce our recent studies aimed at addressing these challenges to improve OCT imagery. First, I will present a self-supervised approach for removing 2D bulk motion artifacts in Optical Coherence Tomography Angiography (OCTA), followed by a self-supervised 3D OCTA image denoising framework. Additionally, I will discuss our work on self-supervised 3D skeleton completion for data extracted from Optical Coherence Doppler Tomography (ODT), as well as our latest exploration of sparse ODT reconstruction using alternative state-space model and attention mechanism.

Bio-Sketch: Haibin Ling received his B.S. (1997) and M.S. (2000) degrees from Peking University, and his Ph.D. (2006) from the University of Maryland, College Park. He worked as an assistant researcher at Microsoft Research Asia (2000–2001), a postdoctoral researcher at the University of California, Los Angeles (2006–2007), and a research scientist at Siemens Corporate Research (2007–2008). He then served as an Assistant Professor (2008–2014) and Associate Professor (2014–2019) at Temple University. From 2019 to 2025, he was a SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University. In 2025, he joined Westlake University, where he is currently a Chair Professor in the Department of Artificial Intelligence. His research interests span computer vision, augmented reality, medical image analysis, machine learning, and AI for science. His honors include the Best Student Paper Award at ACM UIST (2003), NSF CAREER Award (2014), Yahoo Faculty Research and Engagement Award (2019), Amazon Machine Learning Research Award (2019), and Best Journal Paper Award at IEEE VR (2021). He has served as an Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), IEEE Transactions on Visualization and Computer Graphics (TVCG), Computer Vision and Image Understanding (CVIU), and Pattern Recognition (PR). He has also served frequently as an Area Chair for major conferences including CVPR, ICCV, ECCV, ACM MM, and WACV. He is a Fellow of the Institute of Electrical and Electronic Engineers (IEEE).

Neuromorphic Processors for Applications-Aligned High-performance Intelligent Computing

Mohamad Sawan, Dr. Prof., Fellow of the Royal Society of Sciences of Canada (FRSC), a Fellow of the Canadian Academy of Engineering (FCAE), a Fellow of the Engineering Institutes of Canada (FEIC), a Life Fellow of the Institute of Electrical and Electronic Engineers (LFIEEE), and an “Officer” of the National Order of Quebec

Westlake University, Hangzhou, China

Email: sawan@westlake.edu.cn

https://cenbrain.westlake.edu.cn/

Abstract: Neuromorphic Processors are promising computing engines to efficiently address increasing needs for low-power and high-speed data processing. In particular to deliver accelerators for Artificial intelligence applications, and for biomedical high accuracy and safety purposes. We cover in this talk the evolution of brain-inspired neuromorphic computing chips, tracing their progress from the early artificial retina to advanced designs incorporating millions of artificial neurons. We also explore emerging and future opportunities in key applications such as brain-computer interfaces including bio-sensing and bio-actuations, which can facilitate more efficient neural communication and embodied intelligence, which seeks to replicate human cognitive functions; and large-scale models, where these chips' energy-efficient processing capabilities can greatly enhance AI system performance and scalability. For the implementation and validation of these systems, we deal with multidimensional design challenges such as miniaturisation, reliability, safety, self-powered operation, and high-data rate wireless communication. Number of case studies such as seizures detection, Addictions suppression, Vision enhancement, and Language decoding.

Bio-Sketch: Mohamad Sawan is Chair Professor in Westlake University, Hangzhou, China, and Emeritus Professor in Polytechnique Montreal, Canada. He is the founder and director of the Center of Excellence in Biomedical Research on Advances-on-Chips Neurotechnologies (CenBRAIN Neurotech) in Westlake University, and of the Polystim Neurotech Lab in Polytechnique Montreal. He received his Ph.D. degree from the University of Sherbrooke, Canada. He was awarded the Canada Research Chair in Smart Medical Devices (2001-2015) and was leading the Microsystems Strategic Alliance of Quebec (ReSMiQ), Canada (1999-2018). He is Co-Founder, Associate Editor and was Editor-in-Chief of the IEEE Transactions on Biomedical Circuits and Systems (2016-2019). He is Founder and Co-Founder of several International conferences and working groups in bioelectronics such IEEE NewCAS, BioCAS, etc. Dr. Sawan published more than 1000 peer-reviewed journal and conference papers, one Handbook, three books, 13 book chapters, 22 patents, and 40 other patents are pending. He received several awards, among them the Barbara Turnbull Award from the Canadian Institutes of Health Research (CIHR), the J.A. Bombardier and Jacques-Rousseau Awards from the Canadian ACFAS, the Queen Elizabeth II Golden Jubilee Medal, the Medal of Merit from the President of Lebanon, the Chinese National Friendship Award, and the Shanghai International Collaboration Award. Dr. Sawan is a Fellow of the Royal Society of Sciences of Canada (FRSC), a Fellow of the Canadian Academy of Engineering (FCAE), a Fellow of the Engineering Institutes of Canada (FEIC), a Life Fellow of the Institute of Electrical and Electronic Engineers (LFIEEE), and an “Officer” of the National Order of Quebec.

Trustworthy AI

Jeannette M. Wing, Dr. Prof., Member of the National Academy of Engineering (NAE) and the Massachusetts Institute of Technology (MIT) Corporation, Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, Association for Computing Machinery (ACM), Institute of Electrical and Electronic Engineers (IEEE), and National Academy of Innovators

Columbia University in the City of New York, USA

Email: wing@columbia.edu

https://www.cs.columbia.edu/~wing/

Abstract: Recent years have seen an astounding growth in deployment of AI systems in critical domains such as autonomous vehicles, criminal justice, and healthcare, where decisions taken by AI agents directly impact human lives. Consequently, there is an increasing concern if these decisions can be trusted. How can we deliver on the promise of the benefits of AI but address scenarios that have life-critical consequences for people and society? In short, how can we achieve trustworthy AI? Under the umbrella of trustworthy computing, employing formal methods for ensuring trust properties such as reliability and security has led to scalable success. Just as for trustworthy computing, formal methods could be an effective approach for building trust in AI-based systems. However, we would need to extend the set of properties to include fairness, robustness, and interpretability, etc.; and to develop new verification techniques to handle new kinds of artifacts, e.g., data distributions and machine-learned models. This talk poses a new research agenda, from a formal methods perspective, for us to increase trust in AI systems.

Bio-Sketch: Jeannette M. Wing is the Executive Vice President for Research and Professor of Computer Science at Columbia University. She joined Columbia in 2017 as the inaugural Avanessians Director of the Data Science Institute. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Adjunct Professor of Computer Science at Carnegie Mellon where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology. She holds an honorary doctorate of technology from Linkoping University, Sweden.

Professor Wing's current research focus is on trustworthy AI. Her general research interests are in the areas of trustworthy computing, security and privacy, specification and verification,

concurrent and distributed systems, programming languages, and software engineering. She is known for her work on linearizability, behavioral subtyping, attack graphs, and privacy-compliance checkers. Her 2006 seminal essay, titled "Computational Thinking," is credited with helping to establish the centrality of computer science to problem-solving in fields where previously it had not been embraced.

She is currently a member of the American Academy for Arts and Sciences Board of Directors and Council; Computing Research Association Board; American Association of Universities, Senior Research Officers Steering Committee; the Advisory Board for the Association for Women in Mathematics; the Chan-Zuckerberg New York Biohub Steering Committee; and the Empire AI, Inc. Board of Directors. She has been chair and/or a member of many other academic, government, industry, and professional society advisory boards. She was or is on the editorial board of twelve journals, including the Journal of the ACM, the Communications of the ACM, and the Harvard Data Science Review. She received the CRA Distinguished Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, Association for Computing Machinery (ACM), Institute of Electrical and Electronic Engineers (IEEE), and National Academy of Innovators. She is a member of the National Academy of Engineering (NAE) and the Massachusetts Institute of Technology (MIT) Corporation.

Towards Robust Learning-Based Multimedia Forensics

Jiantao Zhou, Dr. Prof.

University of Macau, China

Email: jtzhou@um.edu.mo

https://www.fst.um.edu.mo/personal/jtzhou/

Abstract: In recent years, the proliferation of sophisticated multimedia generation and manipulation technologies, such as deepfakes and advanced image/video editing tools, has significantly blurred the line between authentic and fabricated content. As multimedia plays an increasingly crucial role in information dissemination, legal evidence, and social interactions, ensuring its integrity has become a pressing concern. How can we effectively distinguish genuine media from skillfully crafted forgeries, especially when traditional forensic techniques struggle to keep pace with rapidly evolving tampering methods? Moreover, the challenges are compounded by the degradation of forensic features during media transmission and the vulnerability of detection models to adversarial attacks. In the realm of multimedia forensics, learning-based approaches offer a promising avenue for tackling these complex issues. However, there is a pressing need to enhance their robustness against various distortions, obfuscation strategies, and dynamic threats. This talk explores the latest advancements in robust learning-based multimedia forensics, delving into novel methodologies designed to fortify detection capabilities. From developing innovative feature extraction techniques that can withstand transmission-induced degradation to creating resilient models that can counter adversarial manipulations, the presentation aims to outline a comprehensive research direction for achieving reliable and trustworthy multimedia forensics in an increasingly digital and deceptive world.

Bio-Sketch: Jiantao Zhou is the Head & Full Professor at the Department of Computer and Information Science, Faculty of Science and Technology and the State Key Laboratory of Internet of Things for Smart City, University of Macau, where he also serves as the Interim Director for Research Services and Knowledge Transfer Office. He graduated from the Hong Kong University of Science and Technology in 2009 with a PhD in Electrical and Computer Engineering. He was a Fulbright Junior Scholar at the University of Illinois at Urbana-Champaign (UIUC). Professor Zhou’s research focuses on AI security, multimedia information privacy protection and forensics, and intelligent multimedia information processing. He has published more than 200 papers in top journals such as IEEE T-PAMI, IEEE T-IP, IEEE T-SP, IEEE T-IFS, IEEE T-AC and other top conferences such as CVPR, ICCV, ICML, and AAAI. He currently serves as the Associate Editor for IEEE Trans. Multimedia and IEEE Trans. Dependable and Secure Computing, the top journals in the field of multimedia information processing and security, and was the Editor-in-Chief of APSIPA Newsletters. He is the Chair-Elect for the Multimedia Systems and Applications Technical Committee in IEEE Circuits and Systems Society, and was the TPC Co-Chair of ICME 2023 and the General-Chair of APSIPA ASC 2024. He received the 2022 Macau Science and Technology Award (Third Prize, Natural Science Award) and the 2023 Alibaba Outstanding Academic Cooperation Project Award.