School of Medicine
Showing 11-20 of 66 Results
Senior Research Scientist, Biomedical Data Science
Current Role at StanfordPharmGKB Director
Mark R. Cullen, MD
Director, Center for Population Health Sciences, Professor of Medicine, of Biomedical Data Science, of Health Research & Policy & Senior Fellow at the Stanford Institute for Economic Policy Research
Current Research and Scholarly InterestsSocial and environmental determinants of health; role of workplace physical environment and work organization as causes of chronic disease and disability
Postdoctoral Research Fellow, Biomedical Data Sciences
Resident in Dermatology
BioI am interested in bridging new technologies such as genomics and machine learning with clinical medicine. I am also interested in the use of Twitter for scientific communication and medical education. I am on Twitter: @RoxanaDaneshjou.
Francisco De La Vega
Adjunct Professor, Biomedical Data Science
BioProf. Francisco M. De La Vega is a geneticist and computational biologist with interests in cancer, population, and clinical genomics, and with extensive experience in the life sciences industry. He is a Distinguished Scientific Fellow and Vice President of Bioinformatics and at TOMA Biosciences, a privately held start-up company commercializing a technology for precision oncology derived from inventions at Stanford. Francisco is also Adjunct Professor in the Department of Biomedical Data science of the Stanford School of Medicine, a 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 Steering Committee of 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 yeas 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.
Professor (Research) of Medicine (Biomedical Informatics), of Biomedical Data Science and, by courtesy, of Health Research and Policy
Current Research and Scholarly InterestsDr. Desai is the Director of the Quantitative Sciences Unit. She is interested in the application of biostatistical methods to all areas of medicine including oncology, nephrology, and endocrinology. She works on methods for the analysis of epidemiologic studies, clinical trials, and studies with missing observations.
Max H. Stein Professor and Professor of Statistics and of Biomedical Data Science
Current Research and Scholarly InterestsResearch Interests:
Assistant Professor (Research) of Medicine (Biomedical Informatics) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsComputational systems biology of human disease. Particular focus on integration of high-throughput datasets with each other, and with phenotypic information and clinical outcomes.
Assistant Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsMy lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. We primarily use methods based on regularized linear regression to accomplish this. We primarily focus on applications in oncology and neuroscience.
John A. Overdeck Professor, Professor of Statistics and of Biomedical Data Sciences
Current Research and Scholarly InterestsFlexible statistical modeling for prediction and representation of data arising in biology, medicine, science or industry. Statistical and machine learning tools have gained importance over the years. Part of Hastie's work has been to bridge the gap between traditional statistical methodology and the achievements made in machine learning.