Samuel Maurer
PhD Student - City Planning at UC Berkeley
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolSamuel Maurer is a PhD student in City Planning at UC Berkeley with 11 years of applied experience at the intersection of urban analytics, land use, and municipal finance. He blends academic research with hands-on technical work—contributing to the urbansim platform as a back-end developer and QA engineer to improve model stability, compatibility, and performance. Prior roles at the Federal Reserve Bank of New York and as a municipal finance consultant give him a strong quantitative grounding in capital markets and city fiscal analysis. His work spans mapping and slum research to statistical modeling of cities, reflecting a rare mix of policy, economics, and data-engineering skills. Based in San Francisco, he leverages open-source collaboration and cross-disciplinary methods to translate complex urban systems into practical analytical tools.
11 years of coding experience
BA, Economics, BA, Economics at Amherst CollegeMPA, Economics, Urban Planning, MPA, Economics, Urban Planning at Princeton University
Github Skills (7)
pandas10
pytest10
python10
testing9
yaml9
statsmodels8
documentation7
Programming languages (5)
Github contributions (5)
UDST/urbansim
May 2018 - May 2020
Platform for building statistical models of cities and regions
Role in this project:
Back-end Developer / QA Engineer
Back-end Developer / QA EngineerContributions:1 release, 52 commits, 13 PRs in 2 years
Contributions summary:Samuel primarily focused on improving the quality and stability of the `urbansim` codebase. Their contributions included fixing typos, updating column list comparisons, and refactoring code to use more modern pandas functions. They also addressed platform compatibility issues by revising tests and updating the documentation. Furthermore, the user updated dependencies to ensure compatibility and improve the overall performance of the project.
regionsstatisticalstatistical-models
UDST/choicemodels
Oct 2016 - Oct 2022
Python library for discrete choice modeling
Contributions:3 releases, 1 review, 263 commits in 6 years 1 month
python-librarypythonagent-based-modelingchoice-modelingstatistical-inference
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