Cazandra Aporbo Cazzy-Aporbo

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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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FDF2F8 ░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░
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 make sure we are building ethical AI. In women’s health, “good enough” models still fail real people, so my work is bias audits, subgroup calibration, and ruthless validation...and then shipping tools people actually use. I like the weird edges of data: tails, drift, the places fairness breaks. I’m stubborn about ethics and practical about delivery. I’ll trade a headline metric for a safer model every time and, I’ll show you why with evidence, not vibes. Why me? I bridge research and production. I write the tests, instrument the monitors, and 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.

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

A7F3D0 ▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓▓
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"

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

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.

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

Statistics & QC

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

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

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.

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

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

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.