The Department of Biomedical Data Science* (DBDS) at Stanford was started in order to create an integrated field of biomedical data science, to recruit emerging talent, to provide outstanding training to postdoctoral scholars and graduate students working in this area, and to provide and cultivate an intellectual home for collaborative research. 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.
*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.
Workshop in Biostatistics (BIODS/STATS 260)
Students can earn 1-2 units of course credit by attending these workshops exploring applications of statistical techniques to current problems in medical science. Fall 2016 speakers include: Benedict Paten (UCSC), Hamsa Sridhar Bastani (Stanford), Oriol Nieto (Pandora), Timothy Daley (Stanford), Rachel Wang (Stanford), Elizabeth Purdom (UC Berkeley), Johan Ugander (Stanford), Michal Kosinski (Stanford), and Mark Musen (Stanford).
Study Design Workshop
The SDW provides an opportunity for biomedical researchers who are designing a study to meet with members of the biostatistics staff at Stanford and collaboratively develop a research plan. Researchers leave the SDW with well thought out, specific aims and core methods sections needed for preparing grants and conducting a successful research project.
New DBDS Course: US Healthcare and Big Data Analytics
This Fall 2016 course will be facilitated by Mohit Kaushal M.D. and Carlos Bustamante Ph.D. The course will offer an introductory framework for understanding healthcare configuration encompassing macroeconomics, health policy 101 and market dynamics and focus on the major changes in how (big) data is being applied in industry and why this matters.
New DBDS Faculty
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.
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.
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.