J Bayoán Santiago Calderón

J Bayoán Santiago Calderón

Research Economist

Bureau of Economic Analysis

José Bayoán Santiago Calderón is a research economist in the national economic accounts research group at the Bureau of Economic Analysis. Before joining the federal statistical system, Dr. Santiago Calderón had years of experience in the private sector as a research scientist at various companies. Bayoán also held academic appointments with the Biocomplexity Institute and Initiative at the University of Virginia, where he started his career in public service.

His research has centered on improving decision-making, emphasizing the public good (e.g., science policy). His transdisciplinary research approach has enabled him to routinely collaborate across disciplines and develop a diverse set of domain knowledge and methodological toolset. He also participates in various open-source software communities (e.g., JuliaLang) and civic activism (e.g., Code4PR, Mentes Puertorriqueñas en Accion).

🇵🇷 de pura cepa. I read quite a bit of manga, manhwa & manhua as well as watching anime, donghua and KDramas. Sometimes, I even have time and energy to play some videogames. Check out the relevant profiles: PSN Profiles, MyAnimeList, MyDramaList.


Claremont Graduate University
PhD in Economics 2019
Claremont Graduate University
Claremont Graduate University
MA in Economics 2015
Claremont Graduate University
Southwestern University
BA in Economics 2014
Southwestern University
Minors in Mathematics, Political Science, French, and Chinese.
Semesters abroad in Shanghai, China and Paris, France.


Statistics, Data Science, Machine Learning

Regression Analysis
Scalable Data Analysis


Julia, R, SQL, Git, Linux
Scientific Computing, Software Development
High-Performance Computing, Cloud Computing


Agent-Based Modeling (ABM)
Social Network Analysis
Geographic Information Systems (GIS)
Text Mining, Natural Language Processing (NLP)

Recent & Upcoming Talks

Valuing the U.S. Data Economy Using Machine Learning and Online Job Postings (NBER|CRIW)
Measuring the Cost of Open Source Software Innovation on GitHub


Bureau of Economic Analysis
Research Economist · Full-time
May 2021 – Present Suitland, MD

Working on topics related to the digital economy and measuring intangible assets (e.g., open-source software, data).

Supervisor: David Wasshausen

Pumas-AI, Inc.
Senior Scientist II · Contract
August 2018 – Present Remote

Developed the module for bioequivalence (BE) analysis in the Pumas ecosystem. This included the design, implementation, testing, documentation, maintanence, and coordination with the other components of the ecosystem.

Assisting the consulting branch working on various projects for our clients.

Supervisor: Joga Gobburu, PhD

University of Virginia
Postdoctoral Research Associate · Full-time
May 2019 – May 2021 Arlington, VA

Worked on multiple projects with federal and state agencies helping them meet their missions. These included:

Assisted the infrastructure team on helping the team best use UVA computing resources (e.g., high-performance computing) and best practices (e.g., version control).

Served as project lead and instructor for the Data Science for the Public Good Young Scholars Program (DSPG).

Supervisor: Sallie Ann Keller, PhD

Research Assistant · Contract
June 2018 – May 2019 Remote

Worked on creating and improving the QuantEcon lectures for Julia and its related open source ecosystem (e.g., updating lectures from Julia v0.6 to Julia v1).

Supervisor: Jesse Perla, PhD

Virginia Tech
Data Science for the Public Good Fellow · Contract
May 2018 – August 2020 Arlington, VA

As a fellow for the Data Science for the Public Good (DSPG) program, I worked on two projects:

Supervisor: Gizem Korkmaz, PhD

Michigan State University
Research Fellow · Contract
June 2016 – July 2016 East Lansing, MI

Teaching Assistant for ECSP 891 Advanced Research of the American Economic Association Summer Program.

Supervisor: Lisa DeNell Cook, PhD

Data Scientist · Contract
September 2016 – August 2018 Remote

Worked on three projects:

  • Res-Intel Software Development
  • Behavioral program analyses for Southern California Edison (SCE) (example)
  • California Advanced Homes Program Study (proposal, results)

Supervisor: Hal T. Nelson, PhD

Johns Hopkins University
Teaching Assistant · Contract
June 2015 – August 2015 Baltimore, MD

Fundamentals of Microeconomics (15S.MICO.JHU.1A, 15S.MICO.JHU.2A)

Supervisor: Sean Gibbons

Center for Neuroeconomics Studies
Research Assistant · Part-time
September 2014 – May 2015 Claremont, CA

Assisted the data collection and analysis of several experiments. Some tasks included recruitment, training, running experiments (human and animal subjects). Some of the methods for the data collection and analysis included computer laboratory experiments, drug studies (e.g., alcohol, testosterone), biometric research such as electroencephalogram (EGG) and electrocardiogram (ECG), eye-tracking, and blood work. Several of the tools used included z-Tree and iMotions-BIOPAC.

Supervisor: Paul Joseph Zak, PhD

Intern · Contract
May 2011 – August 2011 San Juan, PR

Summer intern through the Agents of Change Empowerment and Retention Program (PARACa) fellowship, a Mentes Puertorriqueñas en Acción initiative. Worked on the annual report to the state senate on the status of the K-12 public education system titled “El estado actual de las escuelas públicas en Plan de Mejoramiento en Puerto Rico, año escolar 2010-2011”. Assisted the Coalition for Equity and High Quality Education (CECE, for its Spanish acronym) and members of the school community in the choosing and design of the advocacy plan for the year 2011-2012.

Supervisor: David Ortiz


Preparing Future Faculty: Certificate in College Teaching
The certificate in college teaching helps students become an inclusive leader of teaching and learning, connecting you with like-minded faculty who seek to build excellence and foster inclusivity in teaching. Based on the Scholarship of Teaching (SoTL), the program helps students develop pedagogical knowledge and skills through workshops, courses, teaching clinics, and individual consulting on all aspects of teaching and learning, including developing teaching philosophy statements, syllabi, and electronic portfolios.
Statistics with R
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis. You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
See certificate
Machine Learning
This specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. Learners will implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms to real datasets throughout each course in the specialization. They will walk away with applied machine learning and Python programming experience.
See certificate
Fundamentals of Computing
This specialization covers much of the material that first-year Computer Science students take at Rice University. Students learn sophisticated programming skills in Python from the ground up and apply these skills in building more than 20 fun projects. The Specialization concludes with a Capstone exam that allows the students to demonstrate the range of knowledge that they have acquired in the Specialization.
See certificate
Data Science
The Data Science Specialization covers the concepts and tools for an entire data science pipeline. Successful participants learn how to use the tools of the trade, think analytically about complex problems, manage large data sets, deploy statistical principles, create visualizations, build and evaluate machine learning algorithms, publish reproducible analyses, and develop data products.
See certificate