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.

The DBDS Open Rank Faculty Search has begun!

The Department of Biomedical Data Science at Stanford University is recruiting a new faculty member at the Assistant Professor, Associate Professor, or Full Professor level to contribute to the research and educational activities of the Department. The Department was founded in the fall of 2015. 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. Faculty members in the Department are dedicated to the development of novel computational and statistical methods for acquiring, representing, storing, analyzing, and applying biological and clinical data at all scales. We seek applicants with primary interests in those broad areas, who can apply their expertise in biomedical informatics and/or biostatistics to meet departmental goals.

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.

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. 


New DBDS Publication, "Germline determinants of the somatic mutation landscape in 2,642 cancer genomes"

DBDS faculty, Drs Francisco De La Vega and Carlos Bustamante, recently released a paper exploring the work they have been doing with the Pan-Cancer Analysis of Whole-Genomes (PCAWG). 

New Faculty

Aaron Newman, Assistant Professor

Dr. Newman is an Assistant Professor in the Department of Biomedical Data Science and the Institute for Stem Cell Biology and Regenerative Medicine at Stanford University. After earning his Ph.D. in the Biomolecular Science and Engineering Program at the University of California, Santa Barbara in 2010, Dr. Newman moved to Stanford, where he was a postdoctoral scholar and Siebel fellow in the laboratories of Dr. Ash Alizadeh (in close collaboration with Dr. Maximilian Diehn) and Dr. Irving Weissman. As a postdoctoral fellow, Dr. Newman studied the evolutionary history of self/non-self recognition in the immune system, devised novel bioinformatic strategies for the assessment of tumor-derived DNA in the circulation (CAPP-Seq), and introduced a robust computational framework for enumerating cell proportions in bulk tissue samples (CIBERSORT). Dr. Newman is a recipient of a Visionary Postdoctoral Fellowship Award from the Department of Defense and a K99/R00 Pathway to Independence Award from the National Cancer Institute (NIH). His current research focuses on the development of novel data science tools to better understand the clinical significance of neoplastic tissue composition. Projects in the lab typically integrate computational biology with next generation sequencing-based assays, and increasingly involve single cell genomics and experimental techniques.