Careers and Collaboration Speaker Bios 2026

Barbara Engelhardt, PhD

Professor (Research) of Biomedical Data Science and, by courtesy, of Statistics and of Computer Science

Title: STIGMA: Study of Typically Ignored Groups of Menstruating Adults

 

Jason Fries, PhD

Assistant Professor of Biomedical Data Science and of Medicine at Stanford University

Title: Innovating at the intersection of clinical AI and data-centric methods

Abstract: Clinical reasoning requires anticipating future outcomes and responses—an ability that remains challenging for today’s LLMs. In healthcare foundation models, data quality often matters more than data quantity, yet constructing reliable longitudinal patient trajectories from noisy, biased, and incomplete clinical data remains a core challenge. Addressing this challenge requires data-centric approaches to improving training data and feedback loops for effective human–AI teaming

 

Jonathan Chen, MD, PhD

Associate Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science

Title: Artificial Intelligence in Medicine – Integrated Intelligence or Illusory Imitations?

Abstract: Pandora’s box has opened in the form of publicly accessible generative AI systems, now applied to every imaginable—and many unintended—purpose. These emerging technologies challenge the fundamental theorem of informatics: that the combination of human and machine outperforms either alone. When AI alone can match or surpass human–computer teams in complex medical reasoning, we are forced to reconsider the foundations of relevant clinical expertise. Our group is driving the development, evaluation, and deployment of such systems in clinical practice. 

 

Alex Ioannidis, PhD

Assistant Professor of Genetics and of Biomedical Data Science

Title: Integrating Genomics & Phenotype Data for Precision Medicine & Global Health

 

Teri Klein, PhD

Professor (Research) of Biomedical Data Science, of Medicine (BMIR) and, by courtesy, of Genetics

Title: Integrating Pharmacogenomics into Broader Genomic Medicine

Abstract: The success of precision medicine continues to rest on our ability to measure the genome, the environment, the physiological state of patients, and to choose interventions that maximize efficacy and minimize adverse effects. A key component of precision medicine is to understand pharmacogenomics (PGx) – the genetic influences on individual drug response variability. ClinPGx is the premiere online resource whose mission is to curate all known human genetic variation that impacts drug response phenotypes. Based on that resource, we have translated that knowledge into clinical dosing guidelines (CPIC) and routinely annotate genomic data translating this knowledge into direct actions for the patient. Our vision is that all clinical disciplines will routinely use genome-informed prescribing to maximize drug efficacy and minimize adverse events. ClinPGx is an integrated and comprehensive resource for PGx based on the PharmGKB, CPIC, PharmCAT and other resources/tools. ClinPGx complements ClinGen (the Clinical Genome Resource focused on diseases) and provide a unique opportunity to create access to clinical genomics for therapeutic treatment for clinicians in all settings (e.g., academia, community, rural). By collecting, disseminating, and implementing this knowledge across these independent PGx resources, we can bring genomics into everyday clinical care, making it the standard of care.

 

Sylvia Plevritis, PhD

Professor and Chair, Department of Biomedical Data Science Director, Biomedical Informatics Training Program

Title: An AI Framework to Bridge Precision Oncology & Cancer Biology