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Hi! I'm Cody —

LinkedIn Portfolio

Business Administration (Haas) + Data Science @ UC Berkeley

Interested in:

  • reinforcement learning & ML systems
  • quantitative systems & market modeling
  • full-stack product engineering

🛠 Tech Stack

Python TypeScript SQL

PyTorch Gymnasium Stable-Baselines3 Hugging Face

FastAPI Pydantic LangGraph

pandas Next.js React Tailwind CSS

PostgreSQL Redis Docker AWS GitHub Actions


📌 Selected projects

CombatRL · Python · PyTorch · Stable-Baselines3 · Gymnasium

Deterministic, headless-first tactical RL arena with a fully-typed simulation core and replay-based, auditable evaluation.
A shaped 5-stage curriculum takes PPO from passive to a 96.7% win rate; natural-language commands map to bounded behavior profiles without retraining.

AI-Backtest-Lab · Python · FastAPI · Next.js · LangGraph

Event-driven backtesting engine with from-scratch portfolio accounting and risk analytics (Sharpe, drawdown, VaR), plus grid search and walk-forward validation.
A natural-language strategy builder compiles LLM output into a constrained, validated DSL, and a LangGraph research copilot gates every run behind explicit approval.

Government-Workflows · Python · FastAPI · React · Pydantic

Local-first AI workflow tool for municipal finance: deterministic code does every calculation and match, while the LLM is confined to drafting language that must cite source rows.
A validation layer rejects invented numbers or references, and every run is preflighted, logged, and exported as an auditable review packet (758 tests; FastAPI + React).

Vantage · Python · FastAPI · React · Hugging Face

Prediction-market intelligence dashboard that aligns Polymarket price, news sentiment, and whale activity on a single correlation timeline.
Rule-based anomaly and whale detection use explicit, market-local baselines; sentiment inference is lazy, cached, and explainable rather than wired into the hot path.

College-Explorer · Python · FastAPI · Next.js · Postgres

Full-stack decision-support platform with a deterministic ranking engine — normalized weights, hard constraints, and reason-code explanations.
Transparent scoring with no black-box models, surfacing tradeoffs and data confidence instead of a single opaque number.

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