DBDS Mission

We define biomedical data science as the study of biomedical data and information, of how such data and information may be structured, and of how analysis and processing of biomedical data and information will lead to new discoveries and to advances in health and healthcare. 

The Stanford Medical School started the Department of Biomedical Data Science to cultivate and provide an intellectual home for this collaborative research, to recruit emerging talent, and to provide outstanding training to postdoctoral scholars and graduate students working in this area.  As a basic science department, DBDS is devoted to the development of methods for learning from biomedical data, managing those data, and using the data to inform discovery. Our research will create novel computational and statistical methods for acquiring, representing, storing, and analyzing biological and clinical data at all scales.

Workshop in Biostatistics (BIODS/STATS 260)

This seminar series doubles as a class and provides an in depth look at some of the applications of data science in biomedicine. Areas include genetics, evolutionary biology, clinical trials, epidemiology, and others. Speakers are both from academia and industry.

New DBDS Faculty

Francisco De La Vega, Adjunct Professor

Dr. De La Vega is a geneticist and computational biologist with interests in cancer, population, and clinical genomics, and extensive experience in the life sciences industry. Besides being an Adjunct Professor of Biomedical Data Science at Stanford, he is also Distinguished Scientific Fellow and Vice President of Bioinformatics and TOMA Biosciences, a privately held start-up company commercializing a technology for precision oncology derived from inventions at Stanford. He is Director of the International Society of Computational Biology, and is, or has been, a member of the Steering Committee of the NIST-led Genome-in-a-Bottle Consortium, the PanCancer Analysis of Whole Genomes project of the ICGC, and the 1000 Genomes Project. He has more recently contributed to start-up companies in the life sciences area, in positions such as CSO (Annai Systems) and VP of Genomics (Real Time Genetics, Omicia). Previously, he spent over 13 years at Applied Biosystems (later Life Technologies and currently Thermo-Fisher), where he played a pivotal role in the development of several successful genetic analysis technologies. For this, he was inducted in 2009 to the Innovation & Invention Society of Life Technologies, a program that recognized the company’s most elite inventors, and in 2008 was a co-recipient of the Bio-IT World Best Practices Award in Basic Research.


Andrew Gentles, Courtesy Professor

Dr Gentles is an Assistant Professor in the Department of Medicine (Biomedical Informatics Research Institute). Originally, he trained as a theoretical particle physicist in the UK. His more recent research interests are in computational systems biology, particularly the integration and analysis of different types of data, such as genomic data and clinical outcomes.

Much of his recent work has been concerned with the influence of immune infiltrates on outcome in various cancers, and the impact of sub-populations such as cancer stem cells.  He uses statistical and machine learning methods for analyzing genomic data, and extracting insights from large molecular networks by connecting them with phenotypes such as response to treatment and survival outcomes. Dr Gentles confesses to still occasionally perusing the latest news in quantum field theory.


John Ioannidis, Courtesy Professor

John P.A. Ioannidis holds the C.F. Rehnborg Chair in Disease Prevention. He is: Professor of Medicine, of Health Research and Policy, and (by courtesy) of Biomedical Data Science at the School of Medicine; Professor (by courtesy) of Statistics at the School of Humanities and Sciences; co-Director of the Meta-Research Innovation Center at Stanford (METRICS); and Director of the PhD program in Epidemiology and Clinical Research at Stanford.  He is interested in meta-research (the evaluation of scientific practices and how to improve them), large-scale evidence, and research methods, and strongly biased in favor of health and wellness rather than disease and medicine. He is more excited about the Methods sections rather than the Results sections of scientific articles. 


Teri Klein, Professor

Dr. Teri E. Klein is a Professor (Research) in the Department of Biomedical Data Science at Stanford University. Prior to moving to Stanford University in March 2000, she was an Associate Adjunct Professor in the Department of Pharmaceutical Chemistry at the University of California, San Francisco (UCSF). Dr. Klein was recruited by Stanford and offered the opportunity to become the Director of the Pharmacogenomics Knowledgebase (PharmGKB). She received her PhD in Medical Information Sciences from UCSF and undergraduate degrees in Chemistry/Biology from the University of California, Santa Cruz.

Dr. Klein’s training and research programs have been in the forefront and intersection of medicine, computer science, biology, chemistry, pharmaceutics and genetics. Specifically, she is involved in (1) understanding the structural basis and treatment of collagen disorders; (2) impact of genetic variation on drug response for clinicians and researchers; and (3) genomic medicine implementation.


Daniel Rubin, Associate Professor

Daniel L. Rubin, MD, MS is Associate Professor of Biomedical Data Science, of Radiology, of Medicine, and of Ophthalmology (by courtesy) at Stanford University. He is Radiologist, Director of Biomedical Informatics at the Stanford Cancer Institute, Director of the Scholarly Concentration for Informatics and Data Driven Medicine for Stanford Medical School, and member of Stanford's Bio-X interdisciplinary research program.

His NIH-funded research focuses on the intersection of biomedical informatics and imaging science, developing computational methods and applications to extract quantitative information and meaning from clinical, molecular, and imaging data to define imaging phenotypes that can predict underlying tissue biological changes and define disease subtypes. His group translates these methods into practice through applications to improve diagnostic accuracy and clinical effectiveness. 


Data Studio

Come and see how Stanford experts can help you deploy data science in your biomedical research. We offer consultations on clinical trials, population health studies, and complex -omics data. 


Chan Zuckerberg Biohub Awards DBDS Faculty

DBDS Chair Dr. Carlos Bustamante and new DBDS faculty member James Zou are now recipients of the Chan Zuckerberg Biohub Investigator Award! The Biohub "committed more than $50 million to support 47 of the best investigators from Bay Area universities, including 19 from Stanford University. The investigators each receive five-year appointments worth up to $1.5 million to carry out non-conventional scientific exploration and to invent new tools to accelerate the pace of discovery."