Lucas Mccabe
Fellow, Data Science
United States
Summary
👤
Senior🎓
Top SchoolLucas Mccabe is a data scientist and researcher with 11 years of experience applying advanced computational methods to government and academic problems, currently serving as a Fellow in Data Science at LMI while pursuing a PhD in Computer Science at George Washington University. He has progressed through technical and advisory roles at LMI, moving from analyst-level work to principal technical advisor before his current fellowship, demonstrating both hands-on modeling skills and strategic guidance. Lucas is an active contributor to the NetworkX project, where he implemented new graph algorithms and extended functionality—work that highlights deep expertise in graph theory, algorithm design, and scalable backend development. He holds an MS in Applied and Computational Mathematics from Johns Hopkins and a BA in Mathematics and Computer Science from Rutgers, blending rigorous math with practical coding chops. Known for translating theoretical techniques into operational tools for public-sector challenges, he often bridges research rigor with production-ready implementations.
11 years of coding experience
6 years of employment as a software developer
Johns Hopkins UniversityPhD Candidate (In Progress) Computer Science, PhD Candidate (In Progress) Computer Science at The George Washington UniversityBA Mathematics and Computer Science (Minor in Biology), BA Mathematics and Computer Science (Minor in Biology) at Rutgers University
French, English
Github Skills (8)
graph-algorithms10
complex-networks10
graph-analysis10
networkx10
python10
graph-theory10
pytest4
graph-visualization4
Programming languages (2)
Github contributions (5)
networkx/networkx
Mar 2022 - Jul 2022
Network Analysis in Python
Role in this project:
Back-end Developer
Back-end DeveloperContributions:26 reviews, 5 commits, 8 PRs in 4 months
Contributions summary:Lucas primarily contributed to the `networkx` library by implementing new graph algorithms and extending existing functionality. Their work included adding support for multigraphs to the `bridges` algorithm, adding the Tutte and chromatic polynomials, and incorporating the weight parameter for several distance metrics. These changes demonstrate a focus on expanding the library's capabilities in graph analysis and algorithm implementation.
graph-analysispythoncomplex-networksgraph-theorygraph-visualization
Juissie-GW/Juissie.jl
Jan 2024 - Apr 2024
🥝 A Julia-native semantic query engine
Contributions:2 releases, 24 reviews, 22 PRs in 3 months
chatgptembeddingsgemmagptjulia
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