I'm the Chief Data & AI Officer at Loopchii, where I lead the development of human-centered AI systems built for real-world impact. My work focuses on closing the gap between what algorithms promise and what they actually deliver for underserved populations.
Background: I started as a lab scientist in quality control, long before I ever trained a model. That experience shaped how I think about AI: systems should be validated, reproducible, and documented as carefully as any clinical assay. Now, I build AI where failure is expensive, bias is measurable, and the long tail actually matters.
Focus Areas:
- Human-centered AI systems across high-impact domains
- Bias detection and fairness in real-world algorithms
- Bias governance, compliance, and responsible AI regulation
- Pattern discovery in distribution tails (my Serendipity Finder work)
- Bridging research and production
| Degree | Institution | Focus |
|---|---|---|
| MS Data Science | University of Denver | Machine Learning, Statistical Methods |
| BS Integrative Biology | Oregon State University | Chemistry Minor |
| AI in Healthcare Certificate | Johns Hopkins University | Clinical AI Applications (2025) |
2025 ████████████████████████████████ Chief Data & AI Officer @ Loopchii
2025 ██████████████████████████ Head of Data Science @ FoXX Health
2024 ████████████████████ Lead Data Scientist
2023 ██████████████ Data Scientist
2022 ██████████ Quality Control Scientist @ Thermo Fisher
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Advanced framework for detecting extreme-value correlations in distribution tails. Standard regression shows r=0.06; tail analysis reveals r=0.85.
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Analysis frameworks for understanding how consumer health technology serves—and underserves—different populations. Quantifying the gap between marketing claims and clinical reality.
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Quantifying media representation by detecting subtle bias patterns in streaming platforms. Interactive dashboards that reveal what content catalogs actually contain.
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Production-grade Python patterns with comprehensive testing. Bridging the gap between tutorials and real-world code.
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Machine Learning & AI
Data Science & Statistics
Infrastructure & Engineering
The Problem: Most AI fails not because algorithms are wrong, but because human intent gets lost in translation. Goals are messy. Requirements are incomplete. Context is assumed.
Our Approach: We start with intent—the raw, unstructured human need—and loop it through rigorous methodology until it becomes measurable, responsible, and real.
Current Products:
- AuthLoop — Prior authorization AI reducing administrative burden
- WearableLoop — Translating consumer wearable data into clinical insights
- SymptomLoop — Correlating symptoms with biometrics for pattern discovery
Oscar Humberto Montemayor Award · Oregon State University · 2022
- AI Ethics Framework — Comprehensive framework covering bias sources, fairness metrics, and policy landscape for healthcare AI
- Biomimicry Compendium — Research synthesis on nature-inspired design across architecture, materials, and systems (39 academic citations)
- Ethical AI in Healthcare — Presentation on AI strategy for healthcare equity
I'm interested in conversations about:
- Healthcare AI and equity
- Bias detection in clinical algorithms
- Pattern discovery in complex data
- Building responsible AI systems
The best way to reach me is LinkedIn or email.
