Timothy Randolph, PhD

Professor, Clinical Biostatistics, Clinical Research Division
Affiliate Professor, Translational Science and Therapeutics Division
Affiliate Professor, Biostatistics Program, Public Health Sciences Division

Education

University of Oregon, MS, PhD (Mathematics)
University of Puget Sound, BS (Mathematics)

Background

My work focuses on analyzing and interpreting data on biological mechanisms in health, disease, and treatment. I work with a wide variety of investigators helping to quantify their questions and advise on (or develop new) computational methods to aid cancer research. My experience is grounded in mathematics with extensive experience in statistical methods for multivariate data. So, my efforts are driven by the fact that studies of health and disease often involve many, many measurements from genomics, epigenetics, spatial proteomics, metabolic and/or microbial functions. A Ph.D. in mathematics studying functional analysis and operator theory seems distant from statistical analysis of biomedical data, but these concepts have increasing relevance to biomedical data which are becoming more complex, higher dimensional, more structured, and more interrelated. Spectral theory (the mathematics of “inverse” problems) for example, helps summarize the characteristics of multivariate systems. An NIH career-transition grant (K25) brought me from my position as Associate Professor of Mathematics at the University of Missouri in Rolla, MO (now Missouri University of Science and Technology) to the University of Washington and the Fred Hutch.  Collaborations at the Fred Hutch and the UW influence the focus of my efforts on statistical and machine-learning methods for the analysis of microbiome, neuroimaging, metabolomic, proteomic, and genomic data.