Gregory Macfarlane
Assistant Professor at Brigham Young University
Provo, Utah, United States
Summary
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Senior🎓
Top SchoolGregory Macfarlane is an assistant professor of civil engineering at Brigham Young University with 13 years of experience applying large-scale passive data to transportation planning, policy, and simulation. He blends academic research with hands-on practice from roles in consulting and startups, having developed travel-forecasting and simulation-ready models for state and provincial agencies. His technical contributions include extending the popular tidymodels/broom R package to better support spatial and multinomial choice models, making complex model outputs more accessible for analysis. Trained with a Ph.D. in Transportation Systems Engineering and an M.S. in Economics from Georgia Tech, he teaches and designs curriculum while advising practical implementations of passive data streams in forecasting workflows. Colleagues know him for translating advanced modeling into policy-ready insights and for strategic thinking about integrating new data sources into established planning tools.
13 years of coding experience
5 years of employment as a software developer
Ph.D., Transportation Systems Engineering, Ph.D., Transportation Systems Engineering at Georgia Institute of TechnologyBS - University Honors, Civil Engineering, BS - University Honors, Civil Engineering at Brigham Young University
Stackoverflow
2,183reputation
124kreached
18answers
56questions
Github Skills (11)
tidy10
modeling10
tidyr10
r10
rstudio6
knitr6
dplyr6
latex6
ggplot6
roxygen6
combinations6
Programming languages (17)
Github contributions (5)
tidymodels/broom
May 2020 - Oct 2021
Convert statistical analysis objects from R into tidy format
Role in this project:
Data Scientist
Data ScientistContributions:19 commits, 7 PRs, 12 comments in 1 year 5 months
Contributions summary:Gregory primarily focused on developing methods for tidying statistical analysis objects within the `broom` repository. They implemented and refined `tidy`, `glance`, and `augment` methods specifically for `sarlm` and `mlogit` objects. The contributions streamlined the conversion of spatial and multinomial logit model results into a tidy format, enhancing the usability and analysis of these models. These changes expanded the library's support for spatial and choice models.
r-packagestatisticshypothesis-testingregression-modelstidymodels
gregmacfarlane/sppois
Jun 2016 - Jun 2021
Spatial autogressive Poission models in R
Contributions:1 release, 39 commits, 2 PRs in 5 years
r-packagespatialrstats
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