Andrew Gentles, Assistant 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.
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.
Julia Palacios, Assistant Professor
Dr. Julia Palacios is an Assistant Professor in the Departments of Biomedical Data Science and Statistics, starting in Fall 2016. She received her university education from the National Autonomous University of Mexico (BS in Actuarial Sciences), University of California Berkeley (MA in Statistics), and University of Washington (Ph.D in Statistics, 2013).
Julia was a postdoctoral research associate at Harvard University and Brown University, working on mathematical modeling in evolutionary genomics. Her research interests include the estimation of relevant parameters in evolutionary genomics and the development of efficient estimation methods that could impact public health.
Manuel Rivas, Assistant Professor
Dr. Manuel Rivas is an Assistant Professor in the Department of Biomedical Data Sciences at Stanford University. He earned his BSc in Mathematics at MIT, and journeyed across the pond to Oxford University as a Clarendon Scholar where he earned his PhD in Clinical Medicine. He then worked at the Broad Institute, where he was first inspired at the age of 16 to enter the field.
Manny is best known for his work on identifying genetic mutations that protect individuals from common diseases. He will lead a talented team at Stanford that will continue researching common diseases, gaining insights from the human genome that will eventually result in important drug therapies, and working to make the study of these diseases more inclusive to all populations.
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.
James Zou, Assistant Professor
Dr. James Zou is an Assistant Professor of Biomedical Data Science, Computer Science (by courtesy), and Electrical Engineering (by courtesy) at Stanford. He received his Ph.D. from Harvard University in 2014, supported by a NSF Graduate Fellowship. From 2014 to 2016, he was a Simons research fellow at U.C. Berkeley and a postdoc fellow at Microsoft Research. His research has been supported by awards from NSF, Gates Foundation, Simons Foundation.
James develops machine learning algorithms for messy data and applies these new methods to extract disease insights from human population genomics. His work establishes rigorous mathematical foundations for messy data approaches and creates efficient software that has been widely used in disease analysis studies.