Keynotes
Finding Rubies in the Dust: how to exploit errors and interference to enhance systems
Julie A. McCann is a Professor of Computer Systems with Imperial College London
and is currently Co-Director of the School of Convergence Science in Space,
Security and Telecoms and Director of the national CHEDDAR communications
research hub. Formerly Vice Dean Research in the Faculty of Engineering, she has
published extensively on decentralized and self-organizing scalable algorithms
and protocols for Wireless/RF Sensor-based systems, Internet of Things, and
Cyber-physical systems. She leads the Adaptive Emergent Systems Engineering
Research (AESE) research group, and between 2015-2022 was the Deputy Director of
PETRAS IoT Cybersecurity Hub, Critical Ecosystems Lead for the Alan Turing
Institute, and Imperial PI on the EPSRC programme grant Science for Sensor
Systems Software. She has a number of international research collaborations
including Singapore NRF funded Eco-Cities (until March 2024 she had a sub-lab in
Singapore with I2R and HDB), between 2012-2017 directed the Intel Collaborative
Research Institute (ICRI) for Sustainable Cities, and NEC Japan on smart
communications technologies, as well as other projects though EU FP7/H2020
programmes. McCann is an elected Member of the Council of Computer Science
Professors and Heads of Computing, and was elected to the membership committee
of the UKCRC, she holds the 2018 UKRI Suffrage Science Award for Computing and
Mathematics, President’s Medal for Research Excellence 2020, and is a Fellow of
the BCS and Chartered Engineer.
Abstract
TBA
Julie A. McCann
Professor of Computer Systems, Imperial College London, UK
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.
