Doctoral Research Fellow at NYU Stern School of Business
New York, New York, United States
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Summary
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Benjamin Poag is a behavioral researcher and doctoral fellow at NYU Stern with 13 years of experience applying data science to questions about how people respond to and cope with change in organizational settings. He combines rigorous experience sampling and survey methods with practical data engineering—writing Python scripts, cleaning data for SPSS, and using R’s tidyverse and ggplot to turn messy inputs into clear insights for researchers, practitioners, and donors. His work spans academia and community organizations, from designing workplace behavior studies to evaluating youth education programs and social-emotional learning interventions. Benjamin also contributes to open-source tooling for software packaging (notably updates to the cctools package in the spack ecosystem), reflecting an uncommon blend of psychological science and hands-on software maintenance. Based in New York, he brings a systems-oriented perspective to behavioral problems, mapping qualitative stakeholder data into actionable intervention targets.
13 years of coding experience
3 years of employment as a software developer
St. Louis University High
Bachelor of Arts - BA, Psychology (Honors Track), Data Science, Bachelor of Arts - BA, Psychology (Honors Track), Data Science at University of Notre Dame
Doctor of Philosophy - PhD, Management & Organizations, Doctor of Philosophy - PhD, Management & Organizations at NYU Stern School of Business
A flexible package manager that supports multiple versions, configurations, platforms, and compilers.
Role in this project:
Back-end Developer
Contributions:3 reviews, 9 commits, 9 PRs in 1 year
Contributions summary:Benjamin primarily contributed to the `cctools` package within the `spack/spack` repository. Their work involved updating the package definition to incorporate new versions of `cctools`, including versions 7.1.0, 7.1.2, 7.1.3, 7.1.5, 7.1.7, 7.2.1, 7.2.2, 7.2.3, and 7.2.4. These updates included changes to the package's URL, checksums, and dependencies, showcasing an understanding of software packaging and build systems. In addition, the user updated the homepage and URL.
The Cooperative Computing Tools (cctools) enable large scale distributed computations to harness hundreds to thousands of machines from clusters, clouds, and grids.
Contributions:59 releases, 555 reviews, 4337 commits in 10 years 1 month
harnesspythonlarge-scale-distributedgridsmachines
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