Timothy W. Randolph
Ph.D., University of Oregon, Mathematics, 1990.
M.S., University of Oregon, Mathematics, 1984.
B.S., University of Puget Sound, Mathematics, 1982.
Current research focuses on processing, classification and analysis of high-dimensional and/or functional data output from protein mass spectrometry, genetic assays and a variety of spectroscopies and imaging modalities. Other interests include methods for the analysis of networks used to describe gene and/or protein interactions. Additional experience includes lead statistician on biomarker validation studies within the NIH Early Detection Research Network.
A career in mathematics began with a focus on functional analysis, spectral theory of operators and Fredholm theory. Teaching experience as an associate professor includes over 20 different course titles in undergraduate and graduate mathematics and advising for undergraduate and graduate students.
My original research evolved into the study of mathematical control theory, including the study of dynamics and stability of infinite-dimensional linear control systems (those described by system of partial differential and delay equations). An NIH career-transition award provided the opportunity to shift from a research emphasis in pure mathematics to the development of methods for analysis, computation and models of biological/biomedical data.
Society of Industrial and Applied Mathematics
Honors and Awards
1998, Dynamics and Control of Nonautonomous Systems, The Missouri Research Board
1994, Spectral Mapping Properties and Stabilization of Control Systems, The Missouri Research Board
1994-1995, Eigenproperties of Damped Dynamic Systems, The Missouri Research Board
2002-2006, Senior Fellow, University of Washington, Biostatistics
1997, Visiting Associate Professor, University of Missouri-Columbia, Mathematics
1997-2003, Tenured Associate Professor, University of Missouri-Rolla, (Missouri University of Science and Technology)
1991-1996, Assistant Professor, University of Missouri-Rolla, Mathematics & Statistics
1984-1985, Instructor, University of the Pacific, Mathematics
Characterization of the gut microbiome in epidemiologic studies: the multiethnic cohort experience.. Annals of epidemiology. 26(5):373-379.. 2016.
Comprehensive site-specific whole genome profiling of stromal and epithelial colonic gene signatures in human sigmoid colon and rectal tissue.. Physiological genomics. :physiolgenomics.00023.2016.. 2016.
Longitudinal functional models with structured penalties. Statistical Modelling. 16(2):114-139.. 2016.
Enterolignan-producing phenotypes are associated with increased gut microbial diversity and altered composition in premenopausal women in the United States.. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 24(3):546-54.. 2015.
Statistical Methods for Tissue Array Images - Algorithmic Scoring and Co-training.. The annals of applied statistics. 6(3):1280-1305.. 2012.
Structured penalties for functional linear models-partially empirical eigenvectors for regression.. Electronic journal of statistics. 6:323-353.. 2012.
The clathrin adaptor Dab2 recruits EH domain scaffold proteins to regulate integrin β1 endocytosis.. Molecular biology of the cell. 23(15):2905-2916.. 2012.
Prevalidation of Salivary Biomarkers for Oral Cancer Detection.. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 21(4):664-672.. 2012.
Efficient use of longitudinal CD4 counts and viral load measures in survival analysis.. Statistics in medicine. 31(19):2086-97.. 2012.
Detecting genomic aberrations using products in a multiscale analysis.. Biometrics. 66(3):684-93.. 2010.
Occurrence of autoantibodies to annexin I, 14-3-3 theta and LAMR1 in prediagnostic lung cancer sera.. Journal of clinical oncology : official journal of the American Society of Clinical Oncology. 26(31):5060-6.. 2008.
Analytical validation of serum proteomic profiling for diagnosis of prostate cancer: sources of sample bias.. Clinical chemistry. 54(1):44-52.. 2008.
SELDI-TOF MS whole serum proteomic profiling with IMAC surface does not reliably detect prostate cancer.. Clinical chemistry. 54(1):53-60.. 2008.
Signal detection in high-resolution mass spectrometry data.. Journal of proteome research. 7(1):276-85.. 2008.