Biomedical scientist building evidence-based health intelligence. From molecular pharmacology to deployed AI, with published methodology at every layer.
"I turn clinical evidence into health products people can use. Then I publish the methodology so you can verify it."
RIGOR framework. FDA digital health readiness. EU AI Act. Post-deployment monitoring. Clinical AI evidence architecture.
Multi-dimensional safety databases. Evidence tiers. Published methodology with DOIs. API design. Consumer product safety at scale.
AlphaFold modeling. Molecular docking. In silico mutagenesis. Polypharmacology. Chemometrics and analytical validation.
hDAO enzyme modeling. Mast cell activation. Dietary trigger mapping. HS and rosacea connection. DAO deficiency prevalence.
Tyrosinase-TYRP1-TYRP2 melanogenic complex. Pregnancy and breastfeeding skincare safety. OCA genotype-phenotype.
Consumer health product design. Evidence-based telehealth. LDN in dermatology. Regulatory-compliant marketing.
Lactation pharmacology. CMPA cross-reactivity. Infant formula analysis. Postpartum safety. 18 formulas, 15 dimensions.
Prompt Ladder methodology. IRB-filed study. Faculty workshop design. ACS CHED 2026.
Health AI was selected over Amazon, Microsoft, IBM, SAS, NTT Data, Dell, and Oracle for a major enterprise AI governance engagement.
Strategic advisory · Clinical AI validation · Evidence architecture · Research collaboration · Partnerships.
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