Ethical AI in Healthcare

_{My Approach to AI Strategy for Healthcare}

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║  A LIFE IN MISMATCHED PASTELS                                                  ║
║  Every color misaligns just enough to make meaning                             ║
║  Looks accidental—until you see the pattern                                    ║
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FFE0F5 ░░░░░░░░ 2022: Lab coat meets unexpected pastels ░░░░░░░░░░░
D4FFE4 ░░░░░░░░ Where pink met mint met purpose ░░░░░░░░░░░░░░░░░░░░

Name: Cazzy A. Current Role: Head of Data @ FoXX Health Background: Quality Control Scientist → Ethical AI Trajectory: Former lab scientist who traded pipettes for Python Education: - MS Data Science (University of Denver) - BS Integrative Biology & Chemistry (OSU Cascades) - AI in Healthcare Certificate (Johns Hopkins, 2025) Mission: Building AI that addresses healthcare inequities for women Specialty: Pattern discovery in distribution tails & bias detection Philosophy: Every model must be validated, evidence-based & production-ready Approach: Effectiveness + Attractiveness + Impact = Excellence # My ideal palette: Mismatched pastels that shouldn't work but do I started in a lab coat, where I learned that good science means obsessing over validation and reproducibility. Turns out, those habits translate pretty well to machine learning. I’m here to help teams build ethical & sustainable systems. I like the weird edges of data: tails, drift, the places fairness breaks. I bridge research and production. I say “no” when the data can’t support the claim. Bring me the messy dataset you’ve been avoiding; I’ll tell you what the tails are saying, and we’ll make it useful together. If you care less about hype and more about calibration curves, we’ll get along. I like turning messy data into useful, fair systems—models that explain themselves, pass their audits, and still look good in a dashboard. If you’re curious about outliers, tail behavior, and pushing code that doesn’t quietly exclude half the population, say hi.

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D4FFE4 ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒
93C5FD ▒▒▒▒▒▒▒▒ 2019: Data Science emerges ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒
FFCCE5 ▒▒▒▒▒▒▒▒ 30% reduction in errors ▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒

Where I learned that reproducibility is everything

Discovered Python doesn't require safety goggles

Promoted twice Data is my love language

Started fixing bias in medical algorithms

Now designing equitable healthcare AI

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FFCCE5 ▓▓▓▓▓▓▓▓ 2024: Lead Data Scientist ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓
93C5FD ▓▓▓▓▓▓▓▓ Built ML platform from scratch ▓▓▓▓▓▓▓▓▓▓▓▓▓▓

def career_acceleration(): """ The gradient mismatches intentionally. Knowledge compounds in unexpected colors. """ timeline = { "2024_Q1": "Lead Data Scientist", "2024_Q2": "Architected frameworks", "2024_Q3": "AI Engineer", "2025": "Head of Data", "gradient": "exponential", "palette": "Always mismatched" } return "Where patterns emerge from chaos"

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SERENDIPITY FINDER [MY SIGNATURE]

%%{init: {'theme': 'base'}}%% graph TD A[Dataset] -->|r = 0.06| B[Everyone: No Pattern] A -->|But...| C[Me: Check the Extremes] C -->|Top 5%| D[r = 0.85!] C -->|Bottom 5%| E[r = 0.85!] D --> F[PATTERN HIDDEN IN EXTREMES] E --> F F --> G[Being evaluated for drug safety & financial risk] style A fill:#FFE0F5,stroke:#D4FFE4 style C fill:#D4FFE4,stroke:#93C5FD style D fill:#93C5FD,stroke:#FFCCE5 style F fill:#A7F3D0,stroke:#E6E0FF style G fill:#34D399,stroke:#FFE0F5 Loading

PROJECT MATRIX

Velvet Python Coverage in pastels: ░░░░░ Hello (pink) ▒▒▒▒▒ Examples (mint) ▓▓▓▓▓ Tests (yellow) █████ 94% (lavender) Bridging the gap between "Hello World" and production code. Every pattern benchmarked.

Mochi-Moo AI 147 shades of pastel: ░░░░░ Whisper (blush) ▒▒▒▒▒ Think (butter) ▓▓▓▓▓ Create (lilac) █████ Feel (mint) An AI assistant that adapts its thinking style. Built with 147 shades of pastel.

CloudPoof Omega Infrastructure softness: ░░░░░ Concept (mint) ▒▒▒▒▒ Build (peach) ▓▓▓▓▓ Deploy (sky) █████ 12ms (sage) Infrastructure that composes itself. In mismatched pastels.

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MACHINE LEARNING & AI

░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ Machine Learning & AI The stuff that actually ships to production

DATA SCIENCE & VISUALIZATION

▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ Data Science & Visualization Where math meets aesthetics

CLOUD & INFRASTRUCTURE

▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Cloud & Data Engineering Because models need homes too

ADDITIONAL EXPLORATIONS

████████████████████████████████████████████████████████████████████ Programming Explorations Because learning new languages keeps me curious

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Statistics & QC From measuring chemical reactions to measuring algorithmic bias. The lab coat is gone but the hypothesis testing remains.

View Advanced Technical Portfolio

_{Comprehensive technical portfolio showcasing systems architecture, ML engineering, and infrastructure expertise}

View Technical Skills Portfolio

_{Full stack engineering excellence with production-grade implementations}

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THE GRADIENT OF HARM:
░░░░░░░░░░░░░░░░░░░░░░░░░░░ Clinical trials exclude women
▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ 8/10 drugs affect women differently
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ 50% higher misdiagnosis rate
████████████████████████████ Real people harmed daily

MY INTERVENTION (IN MISMATCHED PASTELS):
████████████████████████████ Detect bias (pink on mint)
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Balance data (blue on blush)
▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ Fair models (lavender on sage)
░░░░░░░░░░░░░░░░░░░░░░░░░░░ Healthcare for all (in every shade)

Head of Data @ FoXX Health Leading healthcare equity initiatives Building bias detection frameworks Shipping models that don't exclude All in intentionally mismatched pastels

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THE PASTEL BREATHING: Inhale ░░░░░░░░░░ Soft hypothesis [Pink wonder] Hold ▒▒▒▒▒▒▒▒▒▒ Gentle validation [Yellow patience] Exhale ▓▓▓▓▓▓▓▓▓▓ Mint production [Green growth] Pause ██████████ Sage impact [Deep but soft] This is my palette. Mismatched but intentional. Soft but never weak.

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I'm always interested in conversations about pattern discovery, ethical AI, or why medical algorithms think everyone is a 70kg male. Also happy to discuss career transitions, the beauty of well-documented code, or why pastel color schemes are objectively superior. Topics That Light Me Up Hidden patterns in data • Building fair AI systems • Healthcare innovation Statistical methods that actually work • Open source collaboration Making complex things simple • Women in tech • Ethical technology That one bug that took three days to find (it was a typo)

░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ Curious about patterns?
▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒▒ Interested in fairness?
▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓ Want to build together?
████████████████████████████████████ Let's make AI fair

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THE FINAL COMPRESSION

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║                                                                                   ║
║  COMPRESSION COMPLETE:                                                            ║
║                                                                                   ║
║  Career = ∫(Pink → Blue → Mint → Purpose)dt                                       ║
║                                                                                   ║
║  Every color combination intentionally unexpected                                 ║
║  Pink on mint, blue on blush, lavender on sage                                    ║
║  Mismatched but never unintentional                                               ║
║                                                                                   ║
║  I find patterns in noise                                                         ║
║  I fix bias in algorithms                                                         ║
║  I do it all in mismatched pastels                                                ║
║                                                                                   ║
║  Because different is powerful                                                    ║
║  And unexpected is memorable                                                      ║
║                                                                                   ║
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%%{init: {'theme': 'base', 'themeVariables': {'primaryColor':'#FFE0F5','fontSize':'14px'}}}%% pie title Where My Code Lives "Python (Data Science)" : 45 "Python (ML/AI)" : 30 "JavaScript (Viz)" : 15 "R (Stats)" : 8 "Shell (Automation)" : 2 Loading I write code like I used to write lab reports: obsessively documented, thoroughly tested, and with enough comments that future-me won't hate past-me.

As a statistician specializing in outliers

$$f⁻¹(Me) = ∅ // Unique, no inverse exists x ∈ Outlier ∧ Skew(x) → ∞ // Infinite skewness P(Success) = 1 - P(Giving Up) // Always positive$$