The foundation behind the mission.
Twenty-four years spent on one problem: how do you build a model that is honest about what it knows and doesn’t know? Training in mathematics and physics, an applied-mathematics MS, and a career in model validation and calibrated uncertainty all circle the same discipline. That work now runs on de-identified longevity-cohort data, under a strict local-only governance posture, aimed at Alzheimer’s and ALS.
← Track RecordWho is behind this
The case for data access
The work that bears directly on handling sensitive health data is the de-identified longevity-cohort analysis behind this mission — the NHANES and HRS analyses — carried out under a strict local-only governance posture: data stays on controlled infrastructure, nothing sensitive is shipped to third-party services, and every claim is traceable to its source. The specifics of how that data is handled are documented on the data stewardship page.
Behind that work is the foundation it rests on: training in mathematics and physics, an applied-mathematics MS, and a career spent on model validation, uncertainty quantification, and fidelity trade-offs. The discipline that matters here is the discipline to state confidence intervals that are right, not impressive — a model that says “I don’t know” when it doesn’t know, and calibration that means something beyond a leaderboard number.
Honest neurodegeneration prognosis needs exactly that. Everything built on the molecular track — the conformal prediction intervals, the group-conditional calibration, the honest accounting of where the model is blind — is the direct application of it. This is the same question carried forward, aimed at a more important problem: the mission →
A career built around one question
For twenty-four years I’ve built models that people use to make high-stakes decisions — not in a research lab optimizing benchmarks, but in the field, where the model has to be good enough for someone to stake resources, missions, and sometimes lives on the answer it produces. That constraint changes how you build, validate, and defend a model.
The formative chapter was at MITRE’s Cross-Cutting Urgent Innovation Cell (CUIC), where I served as Chief Analyst — solving problems that cut across the entire Department of Defense through architecture-level trade studies that integrated sensors, platforms, networks, and decision timelines into a single coherent picture. Every one of these was a simplified but faithful model of a complex system, built to answer a question that mattered.
That’s where I developed Analysis Driven Modeling: the analysis question determines which model to build, at what fidelity, with which metrics. Not “here’s a model, let’s find a question for it.” The question always comes first.
Before MITRE, I spent seven years running my own firm. That experience taught me things you can’t learn inside a large organization — finding the work, winning it, and standing behind every answer. When you are the firm, the model has to work and the briefing has to land. After that, you never hand over a number you can’t stand behind.
Today’s AI work is rediscovering what the modeling and simulation community learned decades ago: the hardest part isn’t the model itself. It’s knowing how good the model needs to be for the decision it’s supposed to support. A model that’s impressively complex but not validated against the right metrics is dangerous, not useful — it creates false confidence. The same discipline carries straight over: stating intervals that are honest about what the data can and can’t support, and being explicit about where the model is blind — which is what neurodegeneration prognosis needs.
The full track record
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Major Aerospace Prime — Space DivisionSenior Analyst, Space SystemsMission analysis and simulation for space systems. Trajectory optimization, mission design, and decision-support tooling for space operations. Applying the same fidelity rigor to orbital mechanics and sensor physics that was developed for multi-domain defense analysis.The Model: orbital mechanics, sensor physics, decision-support systemsSpace systems analysis Decision-support tooling Current role (as of 2026)
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The MITRE Corporation — CUICChief Analyst, Cross-Cutting Urgent Innovation CellLed multi-domain analysis teams solving problems that cut across the entire Department of Defense. Effects chain modeling, architecture trade studies, and kill-chain analysis using AFSIM and custom simulation tools. Managed 20+ analysts across concurrent studies. Developed the Analysis Driven Modeling methodology: the analysis question determines the model, not the other way around.The Model: multi-domain operations, effects chains, architecture-level trade spaces20+ analysts managed DoD-wide studies ADM methodology created
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Independent Defense Consulting FirmOwner & Principal AnalystFounded and operated an independent defense analytics firm for seven years, delivering threat analysis, simulation, and systems engineering through subcontract and consulting work with MIT Lincoln Laboratory, CSC, Torch Technologies, and L-3 Coleman Aerospace.
- MDA (longest engagement): threat engineering, architecture-level BMDS analysis briefed to MDA leadership, trajectory and signature modeling validated against intelligence benchmarks
- JIEDDO: lead systems engineer for a counter-IED program; lead author on the system requirements document for a cognitive signature-matching system
- Additional sponsors: ARDEC, AMCOM, NRL
The Model: threat trajectories, missile signatures, BMDS architecture, counter-IED systemsMDA, ARDEC, AMCOM, NRL, JIEDDO Multiple performance awards 7 years as business owner -
Booz Allen HamiltonProgram Manager & Data AnalystProgram management and quantitative analysis for defense and intelligence community clients. Statistical modeling, data visualization, and decision-support tools. Where I first saw the scale at which large organizations use analytic work, and how often the model is fine but the communication fails.The Model: statistical models, decision-support analyticsIC and DoD programs Statistical modeling
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Teledyne Brown EngineeringDefense AnalystMy entry into defense modeling and simulation. Missile defense analysis, trajectory modeling, and physics-based simulation at one of Huntsville’s defense engineering firms. The transition from teaching to defense analysis, where the models have to be right because the consequences are real.The Model: missile trajectories, physics-based defense simulationsMissile defense analysis Entry to defense M&S (modeling & simulation)
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Math Teacher8th Grade · Community College · Professional TutoringStarted as a math teacher and community college instructor. Teaching taught the most important skill in any of this: making sure the person you’re explaining to actually understands what you’re telling them and can act on it. An 8th grader who doesn’t understand your explanation doesn’t politely nod. They stare at you, and you have to find another way in. That skill matters more than any technical credential in every role since.The Model: pedagogical models of student understandingFoundation in education Communication as a core skill
Qualifications & background
Tools & methods
Simulation
- AFSIM
- STK
- STAMP
- POST II
- DICE
- TGx
Programming
- Python
- MATLAB
- SQL
- PyTorch
- TensorFlow
- JavaScript
Methods
- Monte Carlo
- Reinforcement Learning
- Bayesian Inference
- Optimization
- Uncertainty Quantification
- Conformal Prediction
Visualization
- Interactive Dashboards
- 2D/3D Graphics
- Data Visualization
- Canvas API