San Francisco Bay Area · Physician-Epidemiologist
Physician-epidemiologist building evidence and analytics products for healthcare decisions.
I work across real-world evidence, Medical Affairs analytics, safety epidemiology, and clinically grounded AI.
01 · Profile
I care about three things: whether the clinical logic makes sense, whether the analysis can hold up under scrutiny, and whether someone can actually use the result.
I have more than 10 years of experience across real-world evidence, pharmacoepidemiology, healthcare analytics, clinical research, and AI-enabled data products.
At ZS Associates, I lead cross-functional delivery, product strategy, and go-to-market for RWD- and AI-enabled healthcare analytics across biopharma and a national health plan. Previously, I led FDA- and CMS-facing pharmacoepidemiology and safety-surveillance studies using national Medicare-linked data. Earlier work spans pediatric registries, transplantation, oncology, neurology, and cardiovascular outcomes.
02 · Selected signals
Bibliometric metrics current as of July 2026.
03 · Selected impact
I led and contributed to claims-based pharmacoepidemiology using national Medicare-linked data. The work included research on denosumab-related severe hypocalcemia, published in Annals of Internal Medicine and associated with FDA safety communication and labeling action for patients with advanced chronic kidney disease.
I lead cross-functional work across care-gap intelligence, medication adherence, patient journeys, HCP/KOL networks, scientific-insight synthesis, and AI-enabled analytics. I translate clinical questions into product requirements, validation logic, reusable workflows, decision narratives, roadmap choices, and go-to-market materials.
I built and managed a pediatric epilepsy registry covering more than 12,000 patients, connecting structured Epic capture, outcomes reporting, research workflows, quality measurement, and clinical teams.
Selected experience: ZS Associates · The SPHERE Institute · Children’s Healthcare of Atlanta · Emory University
04 · Current questions
How should clinical AI show its evidence and uncertainty?
My focus is evidence traceability, uncertainty, and auditability.
What makes an analytics product useful after the demonstration ends?
Useful products remain legible and relevant inside real workflows.
Where can real-world evidence change a decision earlier?
Earlier evidence can alter study design, field priorities, or safety questions.
05 · Research
06 · Service & education
07 · Contact
Open to conversations about evidence strategy, healthcare analytics, Medical Affairs innovation, clinically grounded AI, and scientific collaboration.