Keynotes
Bio
Ellen Yi-Luen Do (Professor, ATLAS Institute & Computer Science) invents at the intersections of people, design, and technology. She works on computational tools for design, especially sketching, creativity, and design cognition, including creativity support tools and design studies, tangible and embedded interaction and, most recently, computing for health and wellness.
She holds a Ph.D. in Design Computing from Georgia Institute of Technology, a Master of Design Studies from the Harvard Graduate School of Design, and a bachelor’s degree from National Cheng Kung University in Taiwan. She has served on the faculties of the University of Washington, Carnegie Mellon University, and Georgia Institute of Technology. From 2013 to 2016, she co-directed the Keio-NUS CUTE Center in Singapore, a research unit investigating Connected Ubiquitous Technology for Embodiments. Ellen has published over 170 papers and raised over $11M in research funding. She was Design Community Chair for ACM CHI (2012 and 2013), and currently serves on the Steering Committees for ACM Conference series of TEI (Tangible, Embedded and Embodied Interaction, since 2011) and C&C (Creativity & Cognition, since 2017), ISWC (International Symposium on Wearable Computers, since 2018), and AH (Augmented Human, since 2015), and on the editorial board for AIEDAM (Artificial Intelligence for Engineering Design, Analysis and Manufacturing), Springer Series on Cultural Computing, and AUSMT. She enjoys playing the Djembe and performs at festivals and other venues with the Sensua Players.
Title: Mind, Might, and Magic: Creative Technology and Design for Happy, Healthy Living
Abstract:
In this talk, we share research and deployment insights from the intersection of design computing, tangible interaction, and everyday environments to explore how physical materials (Might), computational reasoning (Mind), and playful, human-centered interfaces (Magic) can work together to create responsive and supportive built environments. We discuss how AIoT and smart artifacts provide meaningful interactions, empower individuals through creative toolkits, and support everyday happy, healthy living. By combining physical computing, physical environments, and interacting with large language models, we speculate how adaptive computational intelligence can integrate into our daily lives. We share design patterns and practical deployment strategies for creating accessible, collaborative systems that enrich human capability and foster engagement.
Prof. Ellen Yi-Luen Do
ProfessorDirector, ATLAS Partnerships & InnovationACME LabDESIGN, TANGIBLE INTERACTION, HAPPY HEALTHY LIVINGCOMPUTER SCIENCE ATLAS INSTITUTE
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.
