Keynote Speaker
Title: Rethinking Efficient Artificial Intelligence
Bio
Yan Ming is a Senior Scientist at the Advanced AI Programme of the Agency for Science, Technology and Research (A*STAR, IHPC/CFAR) in Singapore, and serves as the Principal Investigator (PI) for RCA joint projects with Singapore DSO National laboratories. He has led and participated in several major national research projects in Singapore, with a cumulative PI/Co-PI funding of S$16 Million. His research focuses on efficient AI and low-resource natural language understanding. He has published over 30 papers in top-tier conferences and journals such as ACL, AAAI, and IEEE Transactions, and holds four national invention patents. He was shortlisted as a Finalist for Singapore’s MTC Young Investigator Research Grant (MTC-YIRG) He is an Area Chair for ACL 2025, Associate Editor of BMDA (CAS Q1, IF 7.7), Guest Editor of CMC (IF 2.1), and Editorial Board Member of iMeta and CST. He also serves on the Program Committees of major international conferences such as AAAI, IEEE IoP, IEEE UbiSafe, ICCVIT, and ICAMIS.Â
Abstract
Over the past decade, our research has centered on Efficient Artificial Intelligence—the study of how to build capable, robust, and deployable AI systems under constraints of data, memory, compute, and energy. Instead of relying on ever-growing models and massive datasets, our work explores how intelligence can emerge from optimization, structure, and knowledge, especially in low-resource environments. I will firstly introduce the foundations of resource-constrained training and efficient neural computation. Then, extensively explore the data efficiency in natural language processing. Last but not the least, I will introduce our work in efficient AI to multimodal, medical, and real-world systems. Besides the research summary of our previous works, this keynote will also extend to the new treads in Efficient AI: Data-Efficient Intelligence, Tiny Foundation Models, Environment/Energy-aware AI, Multimodal Efficient Reasoning.
Nicos Maglaveras
Professor of Medical Informatics Aristotle University of Thessaloniki Greece
Personalised health driven by digital health systems and multi-source health/environmental data, ML/AI/DL analytics and predictive models
Nicos Maglaveras received the diploma in electrical engineering from the Aristotle University of Thessaloniki (A.U.Th.), Greece, in 1982, and the M.Sc. and Ph.D. degrees in electrical engineering with an emphasis in biomedical engineering from Northwestern University, Evanston, IL, in 1985 and 1988, respectively. He is currently a Professor of Medical Informatics, A.U.Th. He served as head of the graduate program in medical informatics at A.U.Th, as Visiting Professor at Northwestern University Dept of EECS (2016-2019), and is a collaborating researcher with the Center of Research and Technology Hellas, and the National Hellenic Research Foundation.
His current research interests include biomedical engineering, biomedical informatics, ehealth, AAL, personalised health, biosignal analysis, medical imaging, and neurosciences. He has published more than 500 papers in peer-reviewed international journals, books and conference proceedings out of which over 160 as full peer review papers in indexed international journals. He has developed graduate and undergraduate courses in the areas of (bio)medical informatics, biomedical signal processing, personal health systems, physiology and biological systems simulation.
He has served as a Reviewer in CEC AIM, ICT and DGRT D-HEALTH technical reviews and as reviewer, associate editor and editorial board member in more than 20 international journals, and participated as Coordinator or Core Partner in over 45 national and EU and US funded competitive research projects attracting more than 16 MEUROs in funding. He has served as president of the EAMBES in 2008-2010. Dr. Maglaveras has been a member of the IEEE, AMIA, the Greek Technical Chamber, the New York Academy of Sciences, the CEN/TC251, Eta Kappa Nu and an EAMBES Fellow.
Abstract
The last years saw a steep increase in the number of wearable sensors and systems, mhealth and uhealth apps both in the clinical settings and in everyday life. Further large amounts of data both in the clinical settings (imaging, biochemical, medication, electronic health records, -omics), in the community (behavioral, social media, mental state, genetic tests, wearable driven bio-parameters and biosignals) as well as environmental stressors and data (air quality, water pollution etc.) have been produced, and made available to the scientific and medical community, powering the new AI/DL/ML based analytics for the identification of new digital biomarkers leading to new diagnostic pathways, updated clinical and treatment guidelines, and a better and more intuitive interaction medium between the citizen and the health care system.
Thus, the concept of connected and translational health has started evolving steadily, connecting pervasive health systems, using new predictive models, new approaches in biological systems modeling and simulation, as well as fusing data and information from different pipelines for more efficient diagnosis and disease management.
In this talk, we will present the current state-of-the-art in personalized health care by presenting cases from COVID-19 and COPD patients using advanced wearable vests and new technology sensors including lung sound and EIT, new outcome prediction models in COVID-19 ICU patients fusing X-Rays, lung sounds, and ICU parameters transformed via AI/ML/DL pipelines, new approaches fusing environmental stressors with -omics analytics for chronic disease management, and finally new ML/AI-driven methodologies for predicting mental health diseases including suicidality, anxiety, and depression.
