Matt Bartos
Assistant Professor Of Civil Engineering at The University of Texas at Austin
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolMatt Bartos is an Assistant Professor of Civil Engineering at UT Austin who builds smart, autonomous water systems by combining hydrology, infrastructure systems, and software engineering. With 11 years of experience spanning academia and research science, he translates cutting‑edge flow algorithms into reliable tools—authoring and optimizing open‑source Python libraries like pysheds and contributing performance improvements to geopandas' spatial joins. He holds advanced degrees in civil and electrical/computer engineering from the University of Michigan and brings a rare mix of domain expertise and hands‑on backend development. His work not only advances watershed delineation and river profiling but also ensures those methods scale and integrate into real‑world geospatial workflows.
11 years of coding experience
5 years of employment as a software developer
Ph.D., Civil Engineering (Infrastructure Systems), Ph.D., Civil Engineering (Infrastructure Systems) at University of MichiganB.S.E., Civil Engineering (Environmental Focus), 3.80 (Upper), B.S.E., Civil Engineering (Environmental Focus), 3.80 (Upper) at Arizona State University
Github Skills (24)
algorithm10
code-optimization10
algorithms10
python10
pandas10
geographical-information-system10
geospatial10
gis10
hydrology10
optimisation10
numerical-optimization10
optimization10
testing9
data-structure9
unit-testing9
Programming languages (8)
Github contributions (5)
mdbartos/pysheds
Apr 2017 - Feb 2022
:earth_americas: Simple and fast watershed delineation in python.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 releases, 11 reviews, 400 commits in 4 years 11 months
Contributions summary:Matt primarily contributed to the development and maintenance of the pysheds library, focusing on adding new functionalities and optimizing existing algorithms. Their work involved the implementation of the D-infinity flow direction algorithm, along with the refactoring of existing functions to improve performance and correctness. The user also implemented a tool to extract river profiles and contributed to testing efforts for the library's different algorithms, ensuring that the core hydrology related functions are accurate and reliable.
pythongeologyflow-directionhydrologyaccumulation
geopandas/geopandas
Apr 2015 - Apr 2015
Python tools for geographic data
Role in this project:Backend Developer
Backend DeveloperContributions:8 commits, 3 PRs, 4 comments in 7 days
Contributions summary:Matt primarily focused on optimizing the `sjoin` function within the geopandas library, a Python tool for geographic data. Their work involved refactoring the spatial join functionality, improving its performance and ensuring compatibility. They also addressed code style by fixing indentation inconsistencies. The user's contributions improved core spatial join operations, a critical function for the library.
pythongeographygeographic-datapython-toolsgeopandas
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