My work operates at community and agency scale — not the project level. What follows is a view into the domains I work in, the problems I solve, and the capabilities I bring. Specific program details remain appropriately confidential where required.
Quantum research inside Tech Futures generates knowledge that never reaches the solution architects and delivery teams who need it to shape bids — the same chokepoint pattern I watched play out with AI, one level up the tower. I'm building the fix: a Quantum persona inside our internal AI assistant, backed by a knowledge base that translates raw research into business-case-ready intelligence.
The work grew out of my Leadership Academy capstone, From Babel to Blueprint, and is now moving from diagnosis to build: persona architecture, knowledge base ingestion, and a distribution channel to surface the latest research across the organization.
Building on a market value analysis I completed earlier this year, a conversation with leadership reframed the mandate: don't just size the opportunity — own the segment. I'm now scoping what it would take for Tech Futures to establish a durable position in bioscience, from market landscape to the internal business case for where we compete.
Early stage: validating the market thesis, identifying where our existing AI and data capabilities give us a genuine edge, and building the case to bring in front of leadership.
Vast amounts of transit data exist in the public domain — ridership, on-time performance, accessibility metrics, funding flows. Almost none of it is synthesized into a view that actually serves the decision-makers who determine how agencies operate. I built the architecture and analytical frameworks to change that. In two sprints I replaced a manual Tableau workflow with an automated dashboard, taking coverage from 20 agencies to 1,200. Fourteen public datasets now feed a national percentile scoring engine: eight outcomes and 36 weighted KPIs, with community impact metrics alongside the financial ones. The output comes in formats that governance-level stakeholders can act on.
The animating question: what would transit policy look like if it were optimized for the communities it serves, rather than the balance sheets of the agencies running it?
Built AI/ML strategy and data architecture for the Hypersonics community — a domain with reach across Navy, DARPA, and NASA programs that generates enormous volumes of ground test, flight test, and modeling & simulation data. The challenge was not collecting data; it was making it retrievable and useful at the speed the mission demands.
When the Navy HyperLink program had been stalled for three months, I turned it around in two weeks. I architected the AI chatbot and file management solution, the pricing and the sprint plan that got $889M+ in contract value approved and funded. From there I set the connector, persistence and modularity decisions and oversaw ingestion of 540+ GB of hypersonic test and evaluation data. The system scaled to 65 active accounts and cut data correlation from months to seconds.
I was technical point of contact and program manager for five SBIR/STRATFI unmanned-systems programs worth more than $35M. That meant running schedules, test flights, contract and deliverable reviews, payments, and the plans for integrating each program into a program of record.
On the data side, I built the GCP evaluation architecture: vendor test data flowing into BigQuery and Colab, frameworks for comparing design and mission fit, and coordinated with MIT Lincoln Laboratory on drone swarm research and program integration. I also directed a cost and basing analysis. It weighed C-130 crew flight hours and fuel against drone rotation costs, and tested whether drones could run off island flightlines. That analysis informed INDOPACOM logistics planning and contributed to the Secretary of the Air Force's Business Case Analysis.
Restarted and led the SSC Data Stewardship Group — linking 70+ key data stakeholders across SSC, the Department of the Air Force, and the US Space Force to synchronize enterprise data management, analysis, and catalog efforts. Drove the rewrite of the SSC Data Plan, aligning existing data strategy with DoD/DAF/USSF frameworks and establishing LOEs subsequently adopted as standards by two additional field commands.
Analyzed 69 Space Sensing programs to identify data facets and reduce stakeholder meeting overhead from 60 hours to zero in three months.
Limb salvage research is a nascent field — and the existing literature centers predominantly on older male patients. Young women are nearly absent. I am one of the youngest long-term limb salvage patients in the literature, and I've outlived the anecdotal five-year threshold by a significant margin while continuing to regain function.
The research is already running. A daily capacity instrument I built and operate myself is generating the longitudinal dataset: biometrics, subjective body signal, recovery load, and environmental variables, cross-referenced over time in a patient-generated record. The PhD formalizes what the instrument is already producing. The problem has always been clear. Now there is data.