Keynotes

Stanford University, USA

Abstract: Our present healthcare system focuses on treating people when they are ill rather than keeping them healthy. We have been using big data and remote monitoring approaches to monitor people while they are healthy to keep them that way and detect disease at its earliest moment presymptomatically. We use advanced multiomics technologies (genomics, immunomics, transcriptomics, proteomics, metabolomics, microbiomics) as well as wearables and microsampling for actively monitoring health. Following a group of 109 individuals for over 13 years revealed numerous major health discoveries covering cardiovascular disease, oncology, metabolic health and infectious disease. We have also found that individuals have distinct aging patterns that can be measured in an actionable period of time. Finally, we have used wearable devices for early detection of infectious disease, including COVID-19 as well as microsampling for monitoring and improving lifestyle. We believe that advanced technologies have the potential to transform healthcare and keep people healthy.

Biography: Dr. Michael Snyder is the Stanford W. Ascherman Professor and Chair of the Genetics Department at Stanford Medicine. He was recruited by Stanford in 2009 to chair the Genetics Department and direct the Center for Genomics and Personalized Medicine. Under his leadership U.S. News & World Report has ranked Stanford University first in Genetics, Genomics, and Bioinformatics every year for the past decade. As the leading pioneer of 21st century healthcare, Dr. Snyder invented and significantly advanced many industry-standard approaches to personalized medicine. Most recently his research involving longitudinal baseline profiling and state-of-the-art “omic” technologies research has greatly accelerated the advancement of precision medicine. As an entrepreneur, Dr. Snyder’s co-founded companies have collectively raised $242 million in venture capital and are worth more than $6 billion in value. Dr. Snyder also serves on the board for a number of other companies.

National Technical University of Athens, Greece

Abstract: Advances in wireless, wearable, and ambient technologies, coupled with advanced data analytics and simulation techniques open-up new opportunities for optimizing treatment and facilitating effective self-disease management. The integration of heterogeneous data sources with existing pathophysiological knowledge and disease models can fuel informed, accurate diagnosis, timely prognosis, and tailored interventions, underpinning precision medicine. In this talk, we will outline approaches for the efficient integration of multidimensional, heterogeneous data and present AI-empowered methods for disease modelling and clinical decision making that are deployed towards enhancing clinical and well-being outcomes. Emphasis will be placed on frameworks and methods that enable AI-based systems to adapt and continue functioning even in the face of unexpected events or challenges like new data or experiences. The incorporation of principles of interpretability will also be discussed as a means of fostering transparency and confidence in AI-driven health solutions.

Biography: Konstantina S. Nikita, Ph.D., M.D., is since 1996 faculty member, and since 2005, full professor at the School of Electrical and Computer Engineering, NTUA. She is director of the Mobile Radiocommunications Laboratory and founder and director of the Biomedical Simulations and Imaging (BIOSIM) Laboratory. She is the Editor-in-Chief of the IEEE Transactions on Antennas and Propagation, Founding Editor-in-Chief of the IEEE Open Journal of Antennas and Propagation, a member of the Editorial Board of IEEE Transactions on Biomedical Engineering, IEEE Journal of Biomedical and Health Informatics, Journal of Biomedical Engineering and Computing. She is a Fellow of the IEEE, a Founding Fellow of the European Association of Medical and Biological Engineering and Science (EAMBES), a Fellow of the American Institute of Medical and Biological Engineering (AIMBE). She serves as chair of the LS7 Consolidator Grant Panel of the European Research Council (ERC), for granting investigator-driven frontier research in the domain of life sciences. She has been a member of the Board of Directors of the Atomic Energy Commission and of the Hellenic National Academic Recognition and Information Center, as well as a member of the Hellenic National Council of Research and Technology and of the Hellenic National Ethics Committee. She is a member of the IEEE-EMBS Technical Committee on Biomedical and Health Informatics (TC BHI), and has served as Chair of the IEEE Greece Section and Deputy Head of the School of Electrical and Computer Engineering of the NTUA. She is the author of the book “Handbook of Biomedical Telemetry” and of more than 200 journal publications, 400 conference proceedings papers and three patents.

Illumina Artificial Intelligence Lab, USA

Abstract: Large scale human genetics cohorts, comprising hundreds of thousands of individuals with medical record data, are now at the leading edge of modern drug discovery pipeline. This strategy aims to improve upon the current low rate of success in pivotal clinical trials by demonstrating evidence of efficacy directly in humans, as opposed to the traditional strategy of testing in mice and other model organisms. Here we demonstrate the use of the latest deep learning technologies to improve interpretation of both protein-coding and noncoding genetic variation in large human genetic cohorts, leading to novel insights for genetic risk prediction and drug target discovery.

Biography: Kyle Farh, MD, PhD, is VP & Distinguished Scientist has been at Illumina since 2015 and leads the Artificial Intelligence lab at Illumina. The AI lab has been responsible to a large extent for the adoption of deep learning in clinical variant interpretation, including the pioneering SpliceAI and PrimateAI-3D algorithms, two widely used AI tools for clinical interpretation of human genetic variants.

He holds a BS in computer science from Rice University, and MD/PhD degrees from Harvard Medical School and the Massachusetts Institute of Technology in molecular biology. He completed his internship and residency in pediatrics and clinical genetics at Boston Children’s Hospital and his postdoctoral fellowship with Mark Daly and Brad Bernstein at the Broad Institute.

Harvard TH Chan School of Public Health || Brigham and Women’s Hospital, USA

Abstract: One of the central tenets of biology is that our genetics—our genotype—influences the physical characteristics we manifest—our phenotype. But with more than 25,000 human genes and more than 6,000,000 common genetic variants mapped in our genome, finding associations between our genotype and phenotype is an ongoing challenge. Indeed, genome-wide association studies have found thousands of small effect size genetic variants that are associated with phenotypic traits and disease. The simplest explanation is that genes and genetic variants work together in complex regulatory networks that help define phenotypes and mediate phenotypic transitions. We have found that the networks, and their structure, provide unique insight into how genetic elements interact with each other and the structure of the network has predictive power for identifying critical processes in health and disease and for identifying potential therapeutic targets. Drawing on examples from TCGA, GTEx, and other large datasets, we will explore the ways in which modeling regulatory networks provides insight into functional changes that can drive cancers and other complex diseases and how they are influenced by biological sex and age.

Biography: John Quackenbush is Professor of Computational Biology and Bioinformatics and Chair of the Department of Biostatistics at the Harvard TH Chan School of Public Health and Professor at the Dana-Farber Cancer Institute. John’s PhD was in Theoretical Physics but a fellowship to work on the Human Genome Project led him through the Salk Institute, Stanford University, and The Institute for Genomic Research (TIGR), before joining Harvard in 2005. John’s research uses massive data to probe how many small effects combine to influence our health and risk of disease. His published work has more than 97,000 citations and among his honors is recognition in 2013 as a White House Open Science Champion of Change. In 2012 he founded Genospace, a precision medicine software company that was sold to Hospital Corporation of America in 2017. In 2022, he was elected to the National Academy of Medicine.

Rice University, USA

Abstract: Machine learning has become an indispensable tool in computational structural biology enabling researchers to predict and analyze the intricate behaviors of biological molecules with unprecedented accuracy and efficiency. This has profound implications for various applications, from drug discovery to personalized medicine. In the realm of adaptive immune system research, machine learning facilitates the decoding of complex interactions between proteins and other molecules, aiding in the development of immunotherapies and vaccines. This talk will discuss our recent work on the analysis of the binding of intracellular protein fragments called peptides to class-I Major Histocompatibility Complexes (MHCs). These interactions are characterized by a strong structural component that is shown to be extremely important in fully explaining peptide-MHC binding and peptide immunogenicity.

Biography: Lydia E. Kavraki is the Noah Harding Professor of Computer Science, professor of Bioengineering, professor of Electrical and Computer Engineering, and professor of Mechanical Engineering at Rice University. She is the director of the Ken Kennedy Institute at Rice University.

Kavraki received her B.A. in Computer Science from the University of Crete in Greece and her Ph.D. in Computer Science from Stanford University working with Professor Jean-Claude Latombe. Her research interests span robotics, AI, and biomedicine. In robotics and AI, she is interested in enabling robots to work with people and in support of people. Her research develops the underlying methodologies for achieving this goal: algorithms for motion planning for high-dimensional systems with kinematic and dynamic constraints, integrated frameworks for reasoning under sensing and control uncertainty, novel methods for learning and for using experiences, and ways to instruct robots at a high level and collaborate with them. [Read More]

Northeastern University, USA

Abstract: Many anticipate that AI will play a pivotal role in supporting the goals of older adults to “age in place” and sustain quality of life and independence. However, designing these technologies requires supporting the actions of older adults alongside their caregivers, spouses, adult children, and healthcare providers while being able to draw on a longitudinal understanding of routines, habits, norms, and values. In this talk, I survey the challenges incumbent in designing AI systems for informal care networks. These challenges include establishing trust, respecting privacy, retaining autonomy, recognizing diverse routines, and combatting disparities. While these challenges are significant, the benefits of designing for care networks are substantial and a multi-stakeholder approach has the greatest potential for long-lasting care. This work now grounds the “use inspired” research for the new NSF AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING).

Biography: Dr. Elizabeth Mynatt is the Dean of Khoury College of Computer Sciences following a 23-year career at Georgia Institute of Technology where she most recently served as a Regents’ Professor Interactive Computing and executive director of the Institute of People and Technology.

Mynatt is an internationally recognized expert in the areas of ubiquitous computing, human-centered computing, health informatics, and assistive technologies. Currently she is co-PI for the NSF AI-CARING Institute and Emory University’s Cognitive Empowerment Program, both with the goal to create longitudinal, interactive AI technologies to empower older adults and their care networks.

Mynatt was selected for induction to the American Academy of Arts and Sciences (ACM) in 2024. In 2015 she became a Fellow of the Association of Computing Machinery (ACM) for contributions to human-centered computing and the development of health information technologies. She has been recognized as a member of the ACM SIGCHI Academy, and an AAAS, Sloan and Kavli research fellow. Mynatt has published more than 100 scientific papers, led grants from the NSF and NIH, and chaired the CHI 2010 conference, the premier international conference in human-computer interaction.