Larry Wigington
Lieutenant Colonel, United States Marine Corps
Last updated: August 2026
Summary
Senior military officer and operations research scientist, currently completing a PhD on GPU-accelerated methods for large-scale stochastic programs, with a focus on investment under uncertainty. Work spans computational research and senior-leader decision support in the Marine Corps.
Education
PhD, Operations Research
Naval Postgraduate School — Monterey, CA
Dissertation: Solving Large-Scale Stochastic Programs with High-Performance Distributed Computers. Stochastic programming, with a focus on investment under uncertainty.
Master of Science, Operations Research
Naval Postgraduate School
Graduate Certificate, Operational Data Science & Statistical Machine Learning
Naval Postgraduate School
Graduate Certificate, High Performance Computing
Naval Postgraduate School
Master of Systems Analysis
Naval Postgraduate School
Bachelor of Science, Criminal Justice
Troy University
Professional Experience
PHDP-T Fellow
Naval Postgraduate School
Competitively selected for the Marine Corps Doctor of Philosophy Technical Program (PHDP-T) — a fully-funded research assignment under Marine Corps orders providing doctorate-level technical expertise in support of senior leader decision-making and long-range capability development. Conducting doctoral research in Operations Research: GPU-accelerated splitting methods for large-scale stochastic programs, with a focus on investment under uncertainty.
- ▸ Developing structure-exploiting GPU implementations of first-order splitting methods for stochastic programs, extending toward multi-stage and nonlinear models
- ▸ Presented research at ICSP 2025 (Paris), the SIAM Northern and Central California Sectional Conference (2025), and SIAM OP26 (Edinburgh, June 2026)
- ▸ Application class is investment under uncertainty — commitments made before a future is revealed, then adapted as information arrives — at scales where discrete scenario selection is the usual substitute for the full stochastic program
Operations Research Analyst
Manpower and Reserve Affairs, Headquarters Marine Corps
Led quantitative analysis supporting manpower policy and personnel system decisions for the Marine Corps' 186,000-person active force.
- ▸ Designed and deployed a Python-based analytical tool for the $2.3 billion BAH appropriation program, reducing financial analyst processing time from 7+ days to under 10 minutes (99%+ reduction)
- ▸ Applied optimization and statistical modeling to officer talent management policy, directly influencing MOS restructuring for the 88XX/8825 technical officer community
- ▸ Delivered analytical products to SES- and flag-level decision makers at HQMC
Administrative and Operations Officer (Progressive Billets)
United States Marine Corps
Progressive operational leadership across tactical, administrative, and strategic billets. Led teams ranging from small tactical units to enterprise-level staff organizations. Managed complex multi-stakeholder operations under time pressure and resource constraint.
Technical Skills
GPU Computing
CUDA, CuPy (RawKernel/RawModule), Numba, Julia GPU kernels, NVIDIA RTX A3000/A6000, cuSolver, cuBLAS
Optimization Solvers
SCS, PDLP, ADMM, Benders Decomposition, Progressive Hedging, Pyomo, HiGHS
Languages
Python, Julia, R, MATLAB
Infrastructure
Linux (Ubuntu), Docker, GitLab CI/CD, Jupyter, LaTeX
Parallel Computing
GPU-native solvers, MPI, high-performance computing
Selected Research
Stochastic Programming for Investment under Uncertainty dissertation · in progress
Stochastic programming for investment under uncertainty: commitments are made before the future is known, then adapted as information arrives. The work studies this problem class at scales where discrete scenario selection is the usual substitute for solving the full stochastic program.
GPU-Accelerated Splitting Methods for Stochastic Programs
Structure-exploiting first-order splitting methods for large stochastic programs. GPU kernels implement shared-pattern products with K, KT, and (I + KTK) on extensive-form structure. The aim is a competitive alternative to scenario decomposition, with the research program extending toward multi-stage and nonlinear stochastic programs. Presented at ICSP 2025 (Paris), SIAM NorCal 2025, and SIAM OP26 (Edinburgh).
Scale and Computational Evaluation
Computational evaluation of structure-aware first-order methods on stochastic programs, measured by time-to-gap and iteration behavior. Later dissertation work addresses inexact inner solves and multi-GPU scale-out. Comparators include structure-blind GPU first-order solvers and scenario decomposition.
Presentations
Wigington, L. "GPU-Accelerated Splitting Methods for Two-Stage Stochastic Linear Programs." SIAM Conference on Optimization (OP26), Edinburgh, Scotland.
Wigington, L. "Solving Stochastic Programs with GPUs: A Literature Review." International Conference on Stochastic Programming (ICSP), Paris, France.
Wigington, L. Poster presentation. SIAM Northern and Central California Sectional Conference.
Publications
Wigington, L. (2023). "Human Capital." Marine Corps Gazette, 107(11), 18–21.
Horner, D., Wigington, L., and Yoshida, R. (2021). "Red Cell Analysis of Mobile Networked Control System Supporting a Ground Force." Center for International Maritime Security.
View Article →Wigington, L. (2021). "Red Cell Analysis for Mobile Networked Control Systems." Master's Thesis, Department of Operations Research, Naval Postgraduate School.