Shannon Kelley is a Senior Operations Research Software Engineer with a Ph.D. in Industrial Engineering and a decade of experience turning advanced optimization research into production-grade systems across supply chain, transportation, and defense. She specializes in making MILP/MINLP and decomposition methods work in near real-time by bridging algorithm design (custom cutting planes, column generation matheuristics) with engineering stacks like Python, C++, Flask, Docker, and AWS. Her work has delivered measurable commercial impact—from $10M+ annual trucking savings and $28M in captured revenue to a platform acquisition and multi-million-dollar operational improvements for Fortune 500 clients. Shannon has a track record of integrating solver-level innovations (custom cuts for CBC/Gurobi) with cloud and HPC deployments to reduce solve times by as much as 69% and accelerate experimentation 100x. Comfortable leading client-facing product roadmaps and mentoring engineers, she thrives where optimization must be usable, auditable, and scalable. Based in Flagstaff, she enjoys turning theoretical advances into dependable services that make optimization the product.
10 years of coding experience
7 years of employment as a software developer
Master of Science - MS Pure Mathematics, Master of Science - MS Pure Mathematics at DePaul University
Doctor of Philosophy - PhD Industrial Engineering, Doctor of Philosophy - PhD Industrial Engineering at Lehigh University
Bachelor of Science - BS Industrial Engineering, Bachelor of Science - BS Industrial Engineering at University of Illinois Urbana-Champaign
Iteratively adds constraints to LP during solve to boost performance
Contributions:12 releases, 34 PRs, 160 pushes in 6 months
solveperformanceboost
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