Shane Flynn - Applied Mathematician

Shane Flynn

Applied Mathematician

United States

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Summary

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Senior

🎓

Top School

Shane Flynn is an applied mathematician and data scientist with a PhD and nearly a decade of experience building numerical optimization, ML, and computational-physics solutions for industry problems from fleet scheduling to embedded vehicle inference. He combines rigorous research—novel sampling methods and basis-set reductions developed in academia—with production ML: real-time gradient-boosted models, neural networks, and LLM/RAG integrations for operational decisioning. His toolkit spans evolutionary algorithms, mixed-integer programming, classical AI, and deep learning, and he has delivered edge-optimized CNNs and 10 Hz dashboarding for stakeholders. Notably, his research produced a general Quasi-Regular sampling approach that reduced basis sizes by ~75%, reflecting a knack for turning mathematical ideas into tangible performance gains. Based in the U.S., he thrives at the intersection of theory and applied systems engineering, translating complex numerical methods into scalable, real-world solutions.

9 years of coding experience

10 years of employment as a software developer

Bachelor of Science - BS Chemistry. Biology. Minor: Mathematics, Bachelor of Science - BS Chemistry. Biology. Minor: Mathematics at UMass Boston

California Institute of Technology

University of California, Irvine

Github Skills (9)

water6

spectra6

uci5

random-walk4

approximation4

crystallography4

fenics4

grid4

grids3

Programming languages (3)

TeXJavaScriptFortran

Github contributions (5)

swflynn/Quasi_Regular_Grids

Apr 2019 - Jul 2021

Numerical Grid Generation Through Quasi-Lennard-Jones Potential

Contributions:1 PR, 143 pushes, 2 branches in 2 years 2 months

quasigrid-generationjoneslennard-jonesgrid

swflynn/RoVibrational_QMC

Feb 2017 - Apr 2019

Contributions:345 pushes, 3 branches in 2 years 2 months

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