Chad Fulton
Principal Economist at Federal Reserve Board
Washington DC-Baltimore Area United States
Summary
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Rockstar🎓
Top SchoolChad Fulton is a Principal Economist with 13 years of experience combining rigorous economic research and scientific computing at the Federal Reserve Board. He holds a Ph.D. in Economics and bridges advanced statistical methods—classical and Bayesian—with practical numerical simulation using Python, MATLAB, R and more. Chad is an active open-source contributor to core scientific Python projects like statsmodels and SciPy, where his work on time-series econometrics and statistical distributions has improved core numerical and modeling capabilities. He excels at independent research and pragmatic engineering, building reproducible analyses and RESTful tools while integrating big-data technologies such as Hadoop and document stores. Colleagues rely on him to find better ways of doing things, from improving estimation algorithms to deploying APIs for analytical workflows. Based in the DC–Baltimore area, he pairs deep technical proficiency with policy-facing economic insight.
13 years of coding experience
4 years of employment as a software developer
Ph.D., Economics, Ph.D., Economics at University of Oregon
Stackoverflow
3,095reputation
179kreached
114answers
0questions
python
top-5%
Github Skills (19)
algorithms10
scipy10
python10
pandas10
statistics10
time-series10
econometrics10
numpy10
statistical-models10
scientific-computing10
linear-algebra10
data-analysis10
cython8
forecast6
forecasting6
Programming languages (5)
Github contributions (5)
statsmodels/statsmodels
May 2013 - Dec 2022
Statsmodels: statistical modeling and econometrics in Python
Role in this project:
Data Scientist & Back-end Developer
Data Scientist & Back-end DeveloperContributions:2 releases, 16 reviews, 1539 commits in 9 years 9 months
Contributions summary:Chad's contributions center around the implementation and enhancement of models for time-series analysis and econometrics using Python. They focused on enabling Cython multivariate constraint, as well as code clean-up and bug fixes. Their contributions directly influenced the core functionalities of the `statsmodels` library, particularly in its handling of statistical models, as well as their application.
forecastingpythonregression-modelsstatsmodelsstatistics
scipy/scipy
Jun 2014 - Jan 2015
SciPy library main repository
Role in this project:Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:20 commits, 10 comments, 2 issues in 6 months
Contributions summary:Chad primarily contributed to the `scipy/scipy` repository by implementing and improving numerical methods related to linear algebra and statistics. This includes adding a discrete Lyapunov transformation solver, enhancing documentation and style, and adding a test case for large matrices. Furthermore, the user introduced Wishart and inverse Wishart distributions, significantly expanding the statistical capabilities of the library. These commits demonstrate a focus on numerical methods and statistical distributions within the scientific computing domain.
scipypythonscientific-computing
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