School of Medicine
Showing 1-5 of 5 Results
Instructor, Biomedical Data Science
BioI use data science and informatics techniques to study human diseases and their impact on population health outcomes and healthcare spending. Also, to enable new knowledge discovery and for the purpose of building next generation informatics tools for population health management and measurement. I bring over fifteen years of experience with large and diverse population health datasets. For example, population-based registers in Denmark and in the US, the Department of Veterans Affairs Corporate Data Warehouse, the Rheumatology Informatics System for Effectiveness, Stanford and UCSF electronic medical records, administrative healthcare claims and activity monitoring data. I have also developed natural language processing tools for a variety of biomedical use cases. Paired with the practical skills and knowledge that I have gained through working within integrated delivery systems across the US, my extensive training in computer science, biology, and health services research uniquely positions me to build next generation tools to support integrated health delivery systems and population health.
As an Instructor in the Department of Biomedical Data Science at Stanford, I manage a small research group, where I mentor all levels of students and advanced trainees, within the School of Medicine and more broadly within the University. I also lead the Stanford Working Group, Stats for Social Good.
John S. Tamaresis, PhD, MS
Biostatistician, Biomedical Data Science
BioDr. Tamaresis joined the Stanford University School of Medicine in Summer 2012. He earned the Ph.D. in Applied Mathematics from the University of California, Davis and received the M.S. in Statistics from the California State University, East Bay. He has conducted research in computational biology as a postdoctoral scholar at the University of California, Merced and as a biostatistician at the University of California, San Francisco.
As a statistician, Dr. Tamaresis has developed and validated a highly accurate statistical biomarker classifier for gynecologic disease by applying multivariate techniques to a large genomic data set. His statistical consultations have produced data analyses for published research studies and analysis plans for novel research proposals in grant applications. As an applied mathematician, Dr. Tamaresis has created computational biology models and devised numerical methods for their solution. He devised a probabilistic model to study how the number of binding sites on a novel therapeutic molecule affected contact time with cancer cells to advise medical researchers about its design. For his doctoral dissertation, he created and analyzed the first mathematical system model for a mechanosensory network in vascular endothelial cells to investigate the initial stage of atherosclerotic disease.
Professor of Biomedical Data Science and, by courtesy, of Statistics
Current Research and Scholarly InterestsMy research interest includes
(1) Survival Analysis and Semiparametric Modeling;
(2) Resampling Method ;
(3) Meta Analysis ;
(4) High Dimensional Data Analysis;
(5) Precision Medicine for Disease Diagnosis, Prognosis and Treatment.
Professor of Biomedical Data Science and of Statistics
Current Research and Scholarly InterestsMy research is in applied statistics and biostatistics. I specialize in computer-intensive methods for regression and classification, bootstrap, cross-validation and statistical inference, and signal and image analysis for medical diagnosis.