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Charleen D Adams, Ph.D.

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Biography
Northern Arizona University, Flagstaff, AZBA2000Speech pathology & audiology
Northern Arizona University, Flagstaff, AZMA-TESL2002Applied linguistics
Johns Hopkins University, Baltimore, MDMPH2012Genetic epidemiology
National Cancer Institute, Division of Cancer Epidemiology & Genetics, Rockville, MDPredoc2013Clinical genetics
University of Washington & Fred Hutchinson Cancer Research Center, Seattle, WAPhD2016Public-health genetics (epigenetics & bioethics)
University of Bristol, Integrative Epidemiology Unit, Bristol, England Postdoc2018Mendelian randomization
City of Hope Cancer Center, Los Angeles, CAPostdoc2020Cancer bioinformatics
Harvard University, Boston, MALead Scientist for SEED & Postdoc for MIPSCurrentMachine learning for reproductive epidemiology & aging
Teal Omics, Cambridge, MAPrincipal Scientist / Consultant11/2023Causal inference with proteomics

Overview
SUMMARY

Well-rounded 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 (Teal Omics), including a 1-2-year research plan and spearheading the acquisition of a cloud-computing platform for big-data analytics. 27 published articles, most first or senior author, and 2 under review. Experience in aging research, population neuroscience, cancer biology, rare and common diseases, chronobiology, and molecular, genetic, psychiatric, environmental, reproductive, integrative, and chronic-disease epidemiology: open to all topics in epidemiology! And all methods, including learning new ones. While specializing in integrative approaches mentioned above, I choose methods depending on the question, the data, and my collaborators’ interests and expertise: I’m for team science. I collaborate with investigators in statistical genetics, biology, ethics, psychology, and more.

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, 3 governmental agencies, & 4 universities.
• Teaching Experience: 3 universities.
• Data Science & Translational Research: Applied statistical genetics & molecular epidemiology.
• Machine Learning: Prediction & novel biomarker discovery.
• Main Epidemiological Content Areas: cancer, aging, metabolic, neuroscience, chronic disease, reproductive, & integrative.
• Coding Skills: R, command line, & high-performance computing (HPC).
• Consulting: State of Washington's Newborn Screening Program & a biotech start-up, providing R&D guidance.

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Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.