The NLM Colloquia on Biomedical Data Science and Computational Biology Research is a regularly scheduled series of scientific lectures presented by the NLM Division of Intramural Research (DIR), a premier hub of innovation for computational biology and biomedical data science.
The NLM DIR invites experts from outside NLM to present at the Colloquia, where they can share their insights with research communities across NIH and worldwide in the rapidly evolving fields of biomedical data science and computational biology research, as well as how their work impacts these topic areas.
2026 NLM Colloquia Series
The National Library of Medicine (NLM) is pleased to announce the 2026 Ada Lovelace Computational Health Lecture. This lecture series, presented by NLM’s Division of Intramural Research, recognizes the contributions of computer scientists in research on health and biomedicine. The series features women in STEM sectors and invites them to share their pioneering research with NIH and beyond. For this year’s lecture, NLM welcomes Casey Overby Taylor, PhD, Associate Professor of Medicine-General Internal Medicine and Biomedical Engineering in the Johns Hopkins School of Medicine, and the Associate Director of the Institute for Computational Medicine.
Translating the Unfamiliar into Action: Lessons from Genomic Medicine for Clinical AI
Event Date: Thursday, October 29, 2026
Time: 11:00am–12:00pm
Speaker: Casey Overby Taylor, PhD
Location: NLM Lister Hill Center Auditorium, building 38A, and virtual via NIH Videocast
Abstract:
Clinical AI is being implemented with increasing speed, but success depends on more than model performance. Many systems generate recommendations from complex, non-intuitive patterns that clinicians must interpret and act on in practice. In this presentation, I will draw lessons from genomic medicine to discuss how complex model outputs can be translated into care. I will highlight implementation lessons that offer a practical framework for implementing clinical AI in ways that are usable, trustworthy, and actionable.
Speaker Bio:
Casey Overby Taylor is Associate Professor of Medicine-General Internal Medicine and Biomedical Engineering in the Johns Hopkins School of Medicine, and the Associate Director of the Institute for Computational Medicine. She has joint appointments in the Department of Health Policy and Management in the Johns Hopkins Bloomberg School of Public Health, and the Computer Science Department in the Johns Hopkins Whiting School of Engineering. Her research brings together biomedical informatics, biomedical data science, and comparative effectiveness research methods to study effective ways to incorporate digital health technologies into clinical research and clinical care, particularly in delivering guidance for genomic medicine practice. Taylor was a previous recipient of a Johns Hopkins Malone Center for Engineering in Healthcare Fellowship (2017-2020), and of a Microsoft Digital Investigator Fellowship (2020-2022). She was also honored as recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) in 2025.
How to Join:
Location: NLM Lister Hill Center Auditorium, building 38A
This talk will also be broadcast live: NIH Videocast
Interpreting services are available upon request. Individuals with disabilities who need reasonable accommodation to participate in this lecture should contact NLMColloquia@nih.gov or the Federal Relay (1-800-877-8339).
Questions during the presentation can be sent to: NLMColloquia@nih.gov.
Upcoming Events
Biomedicine in the Age of Foundation Models and Agentic AI
Event Date: Thursday, November 12, 2026
Time: 11:00am–12:00pm
Speaker: Lei Xing, PhD
Location: NLM Lister Hill Center Auditorium, building 38A, and virtual via NIH Videocast
Abstract:
Foundation Models (FMs) and agentic AI are revolutionizing biomedical research and medicine, offering a critical path to breakthrough discoveries and substantially improved patient care. In this talk, I will present an overview of this rapidly evolving field and highlight the success of agentic AI in biomedicine. Potential issues and possible solutions related to modern AI will also be discussed. In particular, the important role of prior system knowledge in deep learning models and data-driven decision-making processes will be emphasized. I will argue that physics- and biology-informed AI should be an indispensable part of future biomedical AI, as it substantially improves computational efficiency, accelerates scientific discoveries, and advances personalized medicine.
Speaker Bio:
Dr. Xing is currently the Jacob Haimson & Sarah S. Donaldson Professor of Medical Physics and Director of Medical Physics Division at Stanford University. He also Professor of Electrical Engineering, Computational and Mathematical Engineering. His research has focused on medical imaging, AI in biomedicine, and image-guided interventions. Dr. Xing is an author of more than 500 peer reviewed publications in high impact journals. He is a fellow of AAPM, ASTRO, and AIMBE. He is the recipient of the 2023 Edith Quimby Lifetime Achievement Award of AAPM.
This talk will also be broadcast live: NIH Videocast
Understanding transcription factor binding determinants using neural networks
Event Date: Thursday, December 10, 2026
Time: 11:00am–12:00pm
Speaker: Shaun Mahony, PhD
Location: NLM Lister Hill Center Auditorium, building 38A, and virtual via NIH Videocast
Abstract:
The DNA-binding activities of transcription factors (TFs) are influenced by both intrinsic sequence preferences and extrinsic interactions with cell-specific chromatin landscapes and other regulatory proteins. Disentangling the roles of sequence and chromatin determinants of TF-DNA binding remains challenging. For example, so-called pioneer factors are TFs that can bind to relatively inaccessible chromatin sites during development. However, pioneer factors often vary their binding targets across cell types, pointing to a combination of intrinsic and extrinsic forces guiding their binding activities. My lab focuses on how sequence and chromatin features combine to influence TF binding activities. I will describe our neural network-based approaches that estimate the relative contributions of intrinsic DNA sequence preference and cell-specific chromatin environments to a TF's DNA-binding activities. I will also demonstrate our new concept activation vector approach for interpreting the predictive features that neural networks learn about TF binding sites. We will particularly focus on a selection of Forkhead domain (Fox) TFs that have similar in vitro DNA-binding preferences. Despite the association between Forkhead domains and pioneering activities, the selected Fox TFs display a wide range of affinities for preexisting chromatin states and bind different DNA targets even when induced in identical chromatin settings.
Speaker Bio:
Dr. Shaun Mahony is a Professor and the Dorothy Foehr Huck and J. Lloyd Huck Chair in Genomics and AI at Penn State University. He received his B.E. in Electronic & Computer Engineering and his Ph.D. in Information Technology (Computational Biology) at the University of Galway in Ireland. He was a Postdoctoral Associate at the University of Pittsburgh (2 years), a Research Scientist in the Computer Science and Artificial Intelligence Laboratory at MIT (5 years), and a visiting scientist in the Department of Stem Cell and Regenerative Biology at Harvard University (1 year). He began his faculty appointment at Penn State in 2012 and is a member of the Department of Biochemistry & Molecular Biology and the Center for Eukaryotic Gene Regulation. His research group focuses on building machine learning applications to understand transcription factor binding specificity, particularly in the context of cell fate decisions. He has published over 65 research articles, and his research is supported by R01 and R35 awards from NIH and a CAREER award from NSF.
This talk will also be broadcast live: NIH Videocast
These events are sponsored by:
Richard Scheuermann, PhD
Scientific Director, Division of Intramural Research, National Library of Medicine