AMIA 2025 Informatics Summit Keynote and Plenary Presentations
Opening Keynote
The Past, Present and Future of Epidemic Forecasting
Roni Rosenfeld, BSc, PhD
Monday, March 10, 2025 | 1:30-3:00 pm
The dynamics of epidemics are inherently more stable and thus more predictable than those of the weather. So why is it that we have a fully developed weather forecasting industry but nothing comparable for epidemics, despite the huge benefits it would bring? In this talk, Roni Rosenfeld , BSc, PhD will review the the (very short) history of epidemic forecasting, assess its current capabilities, and discuss the obstacles to achieving its full potential in the coming decade.
Roni Rosenfeld (BSc, mathematics and physics, Tel-Aviv University; PhD, computer science, Carnegie Mellon University) is a professor of machine learning, language technologies, computer science, and computational biology in the School of Computer Science at Carnegie Mellon University, Pittsburgh, Pennsylvania. He has taught machine learning and statistical language modeling to thousands of undergraduate and graduate students since 1997, and has been a mentor to five post-doctoral students and an advisor to a dozen PhD students and many Masters and undergraduate students. From 2018 till 2024, he served as head of the machine learning department.
Roni’s current research interests are in tracking and forecasting epidemics. The Delphi research group, which he co-founded and co-leads since 2012, has been playing a leading role in the development of epidemic forecasting technology in the U.S., and has been named a National Center for Epidemic Forecasting by the U.S. CDC.
Roni has previously worked in statistical language modeling, speech recognition, human machine speech interfaces, and the use of speech and language technologies to aid international developments. He has published some 150 scientific articles in academic journals and peer reviewed conferences, is a recipient of the Spira Teaching Excellence Award (2017), and twice the recipient of the Allen Newell Medal for Research Excellence (1992, 2022).
Year in Review
Translational Bioinformatics and Data Science
Tuesday, March 10, 2025 | 8:30-10:00 am
The Translational Bioinformatics Year-in-Review (TBI Year-in-Review) is an annual presentation given by Nicholas Tatonetti, PhD, at the AMIA Summit. The talk reviews trends in the translational bioinformatics (TBI) literature, aiming to provide a snapshot of the current state of the field, recognize innovative work, and identify future opportunities. The review process involves triaging and evaluating papers from relevant journals, scoring them based on criteria like informatics novelty, application importance, and "wow factor." The top-scoring papers are highlighted, organized into themed categories inspired by pop songs, and showcased to reflect advancements in linking biological and clinical entities through informatics methods.
Clinical Research Informatics and Informatics Implementation
Wednesday, March 12 | 8:30-10:00 am
Artificial Intelligence and Data Science
Yuan Luo, PhD, FACMI, FAMIA, FIAHSI
Thursday, March 13, 2025 | 8:00-9:30 am
The Health Care AI and Data Science Year In Review is an annual retrospective that highlights the most significant advancements, challenges, and forward-looking trends in the field of Artificial Intelligence (AI) and Data Science within healthcare and biomedical research. This comprehensive overview showcases the collaborative efforts between clinicians, researchers, and technologists, emphasizing innovations in machine learning models, large language models (LLMs), and generative AI (genAI) that have potential to revolutionize patient care, diagnostics, and therapeutic development. It also examines the integration of multimodal data sources and ethical considerations surrounding AI deployment in clinical settings. The review serves as a vital resource for the informatics community, providing insights into the current state of AI and Data Science, identifying gaps in knowledge, and suggesting directions for future research and application in healthcare to improve outcomes and efficiency.