while(alive):
learn()
build()
break_things()
fix_some()
automate(the_boring_parts)
about_me = {
"role": "Health Informatics grad (in progress)",
"focus": "data + ML + making healthcare data make sense",
"currently": "debugging my code & my life",
"promises_made": 0
}- 🩺 I work with health data, ML models & dashboards
- 📊 I try to turn messy clinical data into "something usable"
- ⚙️ I'm leveling up from "it runs on my machine" → automated pipelines
- 🐛 currently debugging my code & my life
languages = ["Python"]
ml_data = ["pandas", "numpy", "scikit-learn", "matplotlib"]
nlp = ["spaCy", "NLTK"]
apps_apis = ["FastAPI", "Streamlit", "Uvicorn"]
data_store = ["SQLite"]
devops = ["Git", "GitHub Actions (CI/CD)", "Jupyter"]
ollama = ["local LLMs"]- 🤖 building ML models (sometimes they behave)
- 📈 creating dashboards that look smarter than me
- 🧬 mining clinical notes for signal with NLP
- 🔁 setting up CI/CD with GitHub Actions so my notebooks stop breaking quietly
repo: python-for-health-informatics
pipeline: GitHub Actions
status: ✅ automated checks on push
next: rolling this out to the rest of the fleet
if code_runs:
print("suspicious 🤨")
else:
print("expected behavior 😌")
"if it works, it's production"