contact [at] lucenguyen [dot] com
Chief AI Officer and co-founder of a stealth B2B SaaS startup, building AI-powered pricing, matching, and scheduling tools for the services industry. I lead all technical development—infrastructure, ML systems, and full-stack engineering.
I solve problems that matter, usually by finding the constraint and fixing it—whether that's satellite bandwidth, procurement processes, or data infrastructure.
At 23, I was hand-selected by the Chief Data Officer of Federal Student Aid to help address data issues identified by the GAO following the 2024 FAFSA crisis—problems serious enough to contribute to the agency head's resignation. At 22, as the only data scientist in my office at the US Forest Service, I built NASA satellite data pipelines for wildfire detection that now inform go/no-go decisions for smokejumpers. I also reformed medical supply procurement for fire camps, getting gauze and scalpels to field surgeons by simplifying systems that weren't working.
In Spring 2024, I was the youngest participant selected for the Wilson Center's Executive Branch AI Lab—the most competitive cycle in program history, with 40 slots from over 250 federal applicants. My cohort included senior officials now serving as Penn Fels faculty and foundation leadership. I've briefed congressional staff and military leadership on AI policy and hold bipartisan references from both Republican and Democratic offices.
I've worked across federal government, startups, and legal tech. I think in terms of systems, constraints, and what actually ships.
Data Scientist, Statistical Research and Modeling Group
Hand-selected by the outgoing CDO to address data infrastructure issues flagged by GAO reports following the FAFSA crisis and leadership transition. Fraud prevention and data governance for the $1.7 trillion federal student loan portfolio.
Data Scientist & Emergency Medical Program Specialist
Full-Stack Data Scientist & AI Engineer
Led interdepartmental AI working group and advised C-suite on ML strategy. Predictive modeling for legal services matching. CI/CD, blue/green deployments, frontend/backend development. Left after seven months when technical direction and leadership didn't align.
Operations Fellow, Strategic Energy Innovations Climate Corps
Transportation operations research for the Western US's second-largest intercity rail operator. Built time series ridership prediction models; wrote training materials still in use at BART.
Research Assistant to Professor Brian L. Frye
Legal scholarship research cited in Federal Courts Law Review. Co-hosted Ipse Dixit podcast (2019-2021), interviewing legal and social science scholars. Work mentioned in Teaching Intellectual Property Law (Jacques & Soetendorp, eds).
M.S. in AI Management (in progress, expected 2027)
Technology management, AI applications, safety-critical systems, cybersecurity.
M.S. in Statistics and Data Science, 4.0 GPA (2022)
Thesis: "Inference Using the Akaike Information Criterion"—a synthesis of information theory, philosophy of science, and applied statistics. Received perfect evaluation. Designed and taught capstone course in information theory.
B.A. in Quantitative Social Science, 3.97 GPA, summa cum laude (2021)
Self-designed interdisciplinary major in philosophy of science and mathematical social science via early admission at age 13. Phi Kappa Phi, Mortar Board Honor Society. Graduate coursework in pure and applied mathematics.
Climate Protection Professional (certified). Published poet. Former professional photographer. Amateur baker. I spend time outdoors when I can.
My work has been shaped by two influences.
The first is Dr. Kenneth Burnham, whose book Model Selection and Multimodel Inference inspired my thesis. He once wrote to me:
"Too much of statistics, and statisticians, are driven by pure mathematics at the expense of useful applied statistics and data science."
I've tried to take that seriously.
The second is Haskell Curry's Outlines of a Formalist Philosophy of Mathematics, which I first read at 11 and have re-read every year since. Curry was a student of Hilbert, who said: "Wir müssen wissen. Wir werden wissen."—"We must know. We will know."
I was admitted to college at 13 because I loved differential equations. But the world is messier than formalisms allow. Turbulent flows and complex systems don't yield to clean axioms. Sometimes the best we can do is good-enough inference—useful approximations that get us closer to understanding.
That's what I try to build: systems that work, explanations that hold, solutions that ship.