- B.S.B.A. in Quantitative Economics & Finance, Mathematics Minor
- Open-source developer — creator of
fedfredandedgar-sec - Research: Reinforcement Learning in Monetary Policy, SVAR modeling, Econometrics, Neural Networks
- Interests: Econometrics • Neural Networks • Machine Learning • Python ecosystem design • Quantitative Research & Development
- Goal: Build scalable, open-source infrastructure for computational economics
- Autonomous monetary policy via RL — replication and extensions of the Bundesbank RL paper (policy rules, non-linear environments, stability tests).
- Code:
Autonomous_Fed - Data tooling:
fedfred
- Code:
- Neural-network macro models — construction of linear and non-linear NARX environments for policy simulation.
- PyTorch components including Nguyen–Widrow initialization, a Torch-native Levenberg–Marquardt optimizer, and Single Hidden Layer Feed Forward Neural Network.
- SEC EDGAR pipelines — clean, typed client for EDGAR submissions and filings with sync/async APIs.
- Package:
edgar-sec• PyPI & conda-forge
- Package:
- Time-series infrastructure — standardized IO, transforms (log/YoY/pc1), and model-ready frames/series.
