SUMMARY
Environmental epidemiologist with a decade-plus experience (7 post-PhD, 10 post-MPH, 16 years total), specializing in genetic signatures to understand environmental (aka, non-nucleotide-based) impacts on health. PhD in Public Health Genetics (University of Washington), MPH in Genetic Epidemiology (Johns Hopkins University). Proven expertise combining Mendelian randomization (MR) and machine learning for causal inference. Recent use of this approach with proteomic-based biological clocks (machine-learning predictors of age). R&D guidance for a biotech start-up, including a 1-2-year research plan and spearheading the acquisition of a cloud-computing platform for big-data analytics.
HIGHLIGHTS
16 years designing & conducting etiological, biomarker, & real-world data (RWD) studies in humans, including integrative “omics” for drug-target identification & drug repurposing.
• Biomedical Research: 3 cancer-research centers, government, & 4 universities.
• Teaching: Experience at 3 universities.
• Data Science & Translational Research: Applied statistical genetics & molecular epidemiology.
• Machine Learning: Prediction & novel biomarker discovery.
• Epidemiological content areas: cancer, aging, metabolic, neurological, reproductive, & environmental.
• Coding Skills: R, command line, & high-performance computing.
• Consulting: State of Washington's Newborn Screening Program & biotech start-up R&D guidance.
Principal Scientist / Consultant, 11/2023, Causal inference with proteomics
Teal Omics, Cambridge, MA
BA, 2000, Speech pathology & audiology
Northern Arizona University, Flagstaff, AZ
MA-TESL, 2002, Applied linguistics
Northern Arizona University, Flagstaff, AZ
MPH, 2012, Genetic epidemiology
Johns Hopkins University, Baltimore, MD
Predoc, 2013, Clinical genetics
National Cancer Institute, Division of Cancer Epidemiology & Genetics, Rockville, MD
PhD, 2016, Public-health genetics (epigenetics & bioethics)
University of Washington & Fred Hutchinson Cancer Research Center, Seattle, WA
Postdoc, 2018, Mendelian randomization
University of Bristol, Integrative Epidemiology Unit, Bristol, England
Postdoc, 2020, Cancer bioinformatics
City of Hope Cancer Center, Los Angeles, CA
Lead Scientist for SEED & Postdoc for MIPS, Current, Machine learning for reproductive epidemiology & aging
Harvard University, Boston, MA