Keynotes
Collaborative AI Research and Applications in the Era of Big Data and “Large” Models
Dejing Dou is a Distinguished Professor at Fudan University, the Chief Scientist of BEDI Cloud, and an Adjunct Professor at Tsinghua University. He was the Chief Data Scientist, Partner and Vice President at BCG in Greater China, He was the Head of Big Data Lab (BDL) and Business Intelligence Lab (BIL) at Baidu Research. He was also a tenured Full Professor at the Computer and Information Science Department at the University of Oregon and led the Advanced Integration and Mining (AIM) Lab. He had been the Director of the NSF IUCRC Center for Big Learning (CBL) from 2018 to 2020. He was a visiting Associate Professor at Stanford Center for Biomedical Informatics Research during 2012-2013. His research areas include artificial intelligence, data mining, data integration, NLP, and health informatics. He applied his research in applications from various domains including clean energy, medical data analysis, drug design, finance, smart city, and social behavior & health etc. Dejing Dou has published more than 250 research papers with more than 13000 Google Scholar citations.
Abstract
In this talk, Professor Dejing Dou will first briefly introduce several milestones in the history of artificial intelligence. He will then discuss the new developments in artificial intelligence, particularly in generative AI and large language models, and their applications in big data, combining his research and applications at the University of Oregon, Baidu Research, Boston Consulting Group, and Fudan University. The main topics include: 1) collaborative deep learning in social network and health informatics, 2) applications of spatio-temporal big data mining in smart cities and clean energy, 3) the use of federated learning in trustworthy AI and large language models, 4) controllable data generation for embodied intelligence, and 5) the implementation of generative AI models in business scenarios.
Dejing Dou
Fudan University, China
Tsinghua University, China
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.
