Barton Baker received a doctorate in Economics from American University, with a focus
on computational macro-finance. After working briefly as an economist, he pivoted to a
role building and architecting software and computational tools for data science and
predictive modeling practitioners. In his current role, he leads a team focused on building
scalable and maintainable data science solutions for clients across multiple industries,
with a particular focus on the finance industry.

Barton has made open source contributions to statsmodels, the leading statistical
modeling library in Python, as well as architecting team and company-wide strategies for
data science and software development. He has built scalable systems for cost-effective
and efficient computations responsible for executing large, impactful financial trades..
Barton has also taught upper level undergraduate and graduate level courses in
derivatives modeling and public finance. He frequently evangelizes in the private sector
on best practices in software development, predictive modeling/data science/AI, and
machine learning operations (MLOps).

Education:
BA (Economics) University of Massachusetts, Amherst MA
Ph.D. (Economics with focus on computational finance) American University, Washington, DC

Area of Teaching Specialization: Machine learning, predictive modeling, software
development, MLOps/DevOps, and econometric modeling,

Course Level: Graduate

Course Taught: DET 520 – Artificial Intelligence and Machine Learning