Nikhil Sunder
Quantitative Economics & Finance · Open-Source Scientific Software
Building open-source infrastructure for computational economics — from raw data acquisition to model-ready analysis.
I am an undergraduate in Quantitative Economics & Finance at the University of Miami Herbert Business School, a Research Fellow at the Intelligent Computer Systems Research Institute, and founder of toros-dev. My work sits at the intersection of econometrics, machine learning, and research-grade software engineering: I build the data infrastructure that quantitative research depends on, and I use it to study non-stationary dynamics and monetary policy.
The through-line is a single stack. Acquisition libraries pull economic and disclosure data from primary sources. Representation layers turn that data into typed, validated structures. Modeling libraries, linear and neural, sit on top. Each piece is released as a durable, documented, independently useful artifact rather than as research code.
Software
Production Python packages on PyPI and conda-forge, built to a consistent standard: full type coverage, strict sync/async parity, defensive validation, OpenSSF Best Practices certification, and archived releases with citable DOIs.
| Project | Role in the stack | Status |
|---|---|---|
fedfred |
FRED / ALFRED / GeoFRED / FRASER client | Released |
edgar-sec |
SEC EDGAR REST API client | Released |
toros |
Self-validating financial data representation | In development |
cultivars |
Bayesian, time-varying, and structural VAR | In development |
ns-sdn |
Neural spectral state-space architecture | In development |
Research
Current work centers on state-space models, spectral decomposition, and reinforcement learning for monetary policy.
- NS-SDN — a neural state-space architecture that decomposes non-stationary time series into a time-varying trend plus adaptive spectral components, published at FLAIRS-39 with a follow-up on adaptive spectral emission heads under review.
- Autonomous Fed — reframing monetary policy as a control problem, benchmarking an RL agent against Hinterlang & Tänzer (Bundesbank DP 51/2021) across linear TVP-SVAR and nonlinear NSSM environments.
Affiliations
ICSRI · toros-dev → · Press & coverage → · About me →
