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Executive AI governance lead under the Chief Data & AI Officer at the nation's largest municipal hospital system. Directing enterprise AI strategy across use case intake, vendor evaluation, contract development, and responsible AI standards. Leading AI risk and model observability pipeline, with operational dashboards and compliance audit toolkits tracking model health, adoption rates, and regulatory compliance across generative AI and predictive use cases. Established data governance operating model with metadata standards, data quality controls, and lineage tracking. Championing AI literacy and governed adoption of integrated tools including generative-AI and LLM tools across clinical and administrative workflows.

Directed technical strategy for the NIH Common Fund Data Ecosystem (CFDE), a federated $50M biomedical data platform spanning 12 NIH centers. Authored NIH’s first Data & AI Strategy, aligning with EO 13960 and the NIST AI RMF to define AI governance implementation measures. Developed AI readiness rubrics integrated with existing grant evaluation frameworks. Led proposal down-selection and authored pay plans for AI and data awardees, totaling $7.2M. Co-led an NIH-wide task force to standardize procurement milestones for AI- and data-enabled research. Promoted low-code platforms and LLM tools to scale responsible AI literacy across scientific teams.

Launched the program’s first enterprise-wide Data Governance Committee and stewardship program across 8 domains (EHR, genomics, surveys, etc.) for a 650,000-participant cohort. Led modernization efforts using Palantir Foundry, delivering an AI audit toolkit and data quality scorecards. Co-developed a privacy-preserving record linkage (PPRL) approach adopted for multi-institutional data use. Increased operational efficiency by 30% through a unified Data & AI strategy, and hosted responsible AI hackathons to operationalize model transparency.

Advised federal clients on AI governance, auditability, and ethical deployment. Developed model risk scoring tools in Deloitte’s internal GenAI sandbox, improving performance on high-stakes use cases by 20%. Co-authored the HHS Trustworthy AI Playbook and secured $15.7M in competitive contracts. Designed Deloitte’s first Responsible AI course and co-created a capstone at UC San Diego on explainability (SHAP, LIME, fairness dashboards). Contributed policy recommendations to EO 13960 and the NIST AI RMF.

Developed NLP algorithms to detect adversarial bioengineering threats under IARPA's FELIX program, achieving >90% accuracy in evaluation. Built ML and bioinformatics pipelines in R, Python, and SQL. Led a $150K seed-funded internal innovation team and reduced genomic data processing costs by $200K. Produced reproducible research documentation using Atlassian tools and collaborated with federal partners including the FBI.

Built high-throughput genome sequencing pipelines to support clinical vaccine trials. Analyzed single-cell and bulk RNA-seq datasets to uncover immune correlates of protection. Deployed SLURM-based compute workflows on HPC environments and authored reproducible R/Python analysis code.

Led FDA 510(k) regulatory submissions for Class II diagnostic devices, including KRAS, BRAF, and CYP2C19 genotyping panels. Served as Biological Safety Officer and developed lab infrastructure, protocols, and safety training for BSL-2 operations. Published a peer-reviewed qPCR assay (T-blocker) capable of detecting somatic mutations down to 0.1% frequency.