Keynote Speakers
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George Demiris, Ph.D. |
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Penn Integrates Knowledge University Professor, University of Pennsylvania |
Speaker Biography:Dr. Demiris has been at the forefront of the intersection of informatics and nursing science, and his work has introduced new and innovative approaches to old problems in gerontology. He is exploring innovative ways to utilize technology and support patients and their families in various settings including home and hospice care. He has conducted numerous federally funded studies and his work has been funded consistently over the years both by the National Institutes of Health (NIH) and the National Science Foundation (NSF). His expertise is also in designing and evaluating “smart home” solutions for aging, and in understanding the potential of wearable devices or digitally augmented residential settings to facilitate passive monitoring and support independence and quality of life for community dwelling older adults. His research provides evidence-based recommendations as to how to design systems that are easily adopted by older adults and integrated in their lives. He has examined the challenges of privacy and obtrusiveness in the context of technology use, and he has provided a comprehensive examination of technical, ethical, and practical challenges associated with the use of technology to support aging.
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Yiran Chen, Ph.D. |
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John Cocke Distinguished Professor of Electrical and Computer Engineering, Duke University |
Speaker Biography:Dr. Yiran Chen received B.S. (1998) and M.S. (2001) from Tsinghua University and Ph.D. (2005) from Purdue University. After five years in the industry, he joined the University of Pittsburgh in 2010 as Assistant Professor and was promoted to Associate Professor with tenure in 2014, holding Bicentennial Alumni Faculty Fellow. He is now the John Cocke Distinguished Professor of Electrical and Computer Engineering at Duke University and serving as the director of the NSF AI Institute for Edge Computing Leveraging the Next-generation Networks (Athena), the NSF Industry-University Cooperative Research Center (IUCRC) for Alternative Sustainable and Intelligent Computing (ASIC), and the co-director of Duke Center for Computational Evolutionary Intelligence (DCEI). His group focuses on the research of new memory and storage systems, machine learning and neuromorphic computing, and mobile computing systems. Dr. Chen has published 1 book and about 600 technical publications and has been granted 96 US patents. He has served as the associate editor of more than a dozen international academic periodicals and served on the technical and organization committees of about 70 international conferences. He is now serving as the Editor-in-Chief of the IEEE Circuits and Systems Magazine. He received 11 best paper awards, 1 best poster award, and 15 best paper nominations from international conferences and workshops. He received numerous awards for his technical contributions and professional services such as the IEEE CASS Charles A. Desoer Technical Achievement Award, the IEEE Computer Society Edward J. McCluskey Technical Achievement Award, etc. He has been the distinguished lecturer of IEEE CEDA and CAS. He is a Fellow of the AAAS, ACM, and IEEE, and now serves as the chair of ACM SIGDA.
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Fei Wang, Ph.D. |
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Professor of Health Informatics. Department of Population Health Sciences. Founding Director. Institute of Artificial Intelligence for Digital Health. Weill Cornell Medicine. Cornell University. |
Speaker Biography:Dr. Fei Wang (https://wcm-wanglab.github.io/) is a Professor in Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medicine (WCM), Cornell University, where he also holds a secondary appointment as a Professor in Department of Emergency Medicine. He is the founding director of the WCM institute of AI for Digital Health (AIDH). His major research interest is AI and digital health. He has published more than 350 papers on the top venues of related areas such as ICML, KDD, NIPS, CVPR, AAAI, IJCAI, Nature Medicine, JAMA Internal Medicine, Annals of Internal Medicine, Lancet Digital Health, etc. His papers have received over 30,000 citations so far with an H-index 81. His (or his students’) papers have won 8 best paper (or nomination) awards at top international conferences on data mining and medical informatics. His team won the championship of the AACC PTHrP result prediction challenge in 2022, NIPS/Kaggle Challenge on Classification of Clinically Actionable Genetic Mutations in 2017 and Parkinson's Progression Markers' Initiative data challenge organized by Michael J. Fox Foundation in 2016. Dr. Wang is the recipient of the NSF CAREER Award in 2018, as well as the inaugural research leadership award in IEEE International Conference on Health Informatics (ICHI) 2019. Dr. Wang also received prestigious industry awards such as the Sanofi iDEA Award (2021), Google Faculty Research Award (2020) and Amazon AWS Machine Learning for Research Award (2017, 2019 and 2022). Dr. Wang’s Research has been supported by a diverse set of agencies including NSF, NIH, ONR, PCORI, MJFF, AHA, etc. Dr. Wang is the past chair of the Knowledge Discovery and Data Mining working group in American Medical Informatics Association (AMIA). Dr. Wang is a fellow of AMIA, a fellow of IAHSI, a fellow of ACMI and a distinguished member of ACM.
Title: AI and Data Science in Computational Health: A Full-Stack Holistic Perspective
Abstract: With the revolution of machine learning technologies in recent years, AI and data science are holding greater promise in understanding diseases and improving quality of care. Computational health is such a research area aiming at developing computational methodologies for deriving insights from various biomedical data. Currently the research in computational health has been mostly siloed, with different communities focusing on analyzing different types of data. However, human health has its own ecosystem with information from all aspects including genome, phenome and exposome. We need to integrate the insights from all of them to have more holistic understandings of diseases. In this talk, I will present the research from my lab health in recent years on building machine learning models for analyzing different types of data involved in different levels of human life science, and the need for transitioning from conventional focused-community based strategy to a holistic full-stack regime for the next-generation health AI/data science research.
