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Watson & Crick 🧬

Science was always a conversation. But now you're in it.

🏆 1st Place — ML & AI Tooling Challenge, DataHacks 2026 (Best Use of Marimo/Sphinx) Built in 36 hours at UC San Diego by Kiruthika Marikumaran & Mallika Dasgupta.


What is Watson & Crick?

Watson & Crick is an AI-powered environmental health research engine. You ask a plain-English question — Does PM2.5 pollution affect Alzheimer's risk? What does wildfire smoke do to my lungs? — and two AI scientists argue about your data in real time, run real statistics, and hand you a peer-review-grade research report in about 30 seconds.

Watson runs the analysis optimistically. Crick questions every assumption. Together they produce:

  • A causal graph showing the pathway from environmental exposure → candidate genes → health outcome (built in D3.js with force-directed layout, edge weights derived from real correlation strength)
  • Three ranked hypotheses with biological mechanisms
  • A structured 8-section research report
  • A confidence score with a full breakdown of every factor and weight
  • Audio playback, PDF export, and a community feed where anyone can publish and fork research

Features

  • 🔬 Research Engine — Pearson correlations, Random Forest models, Isolation Forest anomaly detection, real p-values
  • 🌡️ Live UCSD Campus Safety Dashboard — fuses real Scripps AWN sensor data with EPA PM2.5 and NWS heat index; SAFE/CAUTION/ALERT system with K-Means zone clustering and Random Forest temperature forecasting
  • 📓 Marimo Interactive Notebook — exposes the exact statistical engine with live reactive sliders so anyone can reproduce or challenge a result in real time
  • 🗺️ San Diego Health Map — solar permit density vs. respiratory health outcomes across neighborhoods
  • 💬 Ask the Scientists — follow-up chatbot powered by Gemini 2.5 Flash

Datasets

Dataset Source
Air quality sensors EPA AQS
Climate records NOAA
Disease prevalence CDC PLACES
Genetic variants EMBL-EBI GWAS Catalog
Live campus sensors Scripps Institution AWN @ UCSD

Tech Stack

Frontend: React, Vite, Tailwind CSS, TanStack Router, D3.js, jsPDF

Backend: Python, FastAPI

ML: scikit-learn (Random Forest, Ridge Regression, Isolation Forest, K-Means), SciPy

AI: Gemini 2.5 Flash API, Claude

Notebook: Marimo

Infra: Vercel, Lovable


Getting Started

# Clone the repo
git clone https://github.com/kirustar14/EcoCausal.git
cd EcoCausal

# Backend
cd backend
pip install -r requirements.txt
uvicorn main:app --reload

# Frontend
cd frontend
npm install
npm run dev

You'll need a Gemini API key set as GEMINI_API_KEY in your environment.


Built at DataHacks 2026

DataHacks is the annual 36-hour MLH-certified hackathon hosted by the Data Science Student Society (DS3) at UC San Diego.

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