The Bio3 Seminar Series meets every second and fourth Friday of the month, during the academic year.
*MS and PhD biostatistics students are expected to attend the bi-weekly seminar series, as part of their academic curriculum.*
Seminar Schedule – Fall 2025
Friday, October 10, 2025 at 10:00 am
Hyokyoung “Grace” Hong, Ph.D. Senior Investigator, Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute National Institutes of Health
Abstract: Biomedical studies now generate vast amounts of molecular data, ranging from thousands of biomarkers to millions of genetic variants, creating major challenges in distinguishing meaningful signals from noise. Patient outcomes vary widely, and methods that focus only on averages can overlook biomarkers linked specifically to poor or favorable outcomes. One direction of this work applies quantile regression to study different parts of the outcome distribution, allowing the identification of markers relevant at the extremes. Another challenge arises in the high-dimensional setting, where some biomarkers may not appear important individually but become informative when considered jointly. To address this, I will introduce a conditional screening approach developed to uncover these hidden signals.
The presentation will also include an overview of the NIH Intramural Research Program, which supports postbaccalaureate, predoctoral, and postdoctoral fellows in biostatistics and related fields, working closely with investigators across diverse areas of biomedical research.
Location: Building D, Warwick Evans Conference Room
Friday, September 12, 2025 at 10:00 am
Fuhai Li, Ph.D. Associate Professor, Department of Statistics, Institute for Informatics (I²), & Department of Pediatrics, School of Medicine, and the Department of Computer Science & Engineering (CSE) Washington University in St. Louis
Abstract: Transformative AI models are powerful tools for integrating and mining large-scale biomedical and omics data, and have been revolutionizing research in precision medicine. In this talk, I will present novel transformative AI models that we have developed to combine large language models (LLMs) with graph-based AI to integrate and analyze vast omics datasets for identifying disease targets, inferring signaling pathways, and predicting effective drugs and drug combinations. A key component of the novel AI models is the novel text-numeric graph (TNG) or text-omic signaling graphs (TOSGs), a novel structure in which graph entities and associations carry both textual and numeric attributes. I will also introduce an AI multi-agent system that we have developed to accelerate biomedical discovery by unifying omics data analysis, literature-based deep search, and reasoning to generate novel scientific hypotheses. I will then showcase the applications of these novel AI models with analysis of heterogeneous pharmacogenomics data for precision medicine.
Location: Building D, Warwick Evans Conference Room
The Bio3 Seminar Series are for educational purposes and intended for members of the Georgetown University community. The seminars are closed to the public.