Featured
Founder — Safety Critical Labs
Traditional software assurance assumes deterministic, logic-based systems with stable behavior after deployment. AI doesn't work that way. Safety Critical Labs certifies AI and ML systems against the AI Requirements Framework: requirements and verification methods designed specifically for data-driven, probabilistic systems, covering bias, drift, hallucination, explainability, and human-AI interaction. Applicable across aerospace, aviation, automotive, and medical domains.
Research Appointments
Researcher, Systems Engineering & Integration — Human Spaceflight
A sustained research arc at the intersection of systems engineering and AI assurance for human spaceflight: how do you certify data-driven, probabilistic systems for missions where failure is not an option? The core thread — commercial partner review and AI requirements research on CLDP Systems Integration — was carried out under Hector Chavez's mentorship, alongside applied AI and data systems work delivered through Barrios Technology.
- CLDP Systems Engineering & Integration (2023 — 2025) — Served as NASA SE&I reviewer across four CLDP commercial partners, conducting ~40 reviews in 9 months, including CDPO-delegated lead review authority for Blue Origin; generated RFAs and RIDs reported directly to the Program Office that drove partner-implemented design changes. Led requirements traceability for CLDP, tracing 300+ requirements to CLDP-REQ-1130 and delivering a unified baseline spanning three commercial providers, adopted by the Program Office and NASA.
- AI requirements research (2023 — 2025) — Authored 70 requirements in a new AI requirements framework for safety-critical spaceflight systems, with SME consultation from the University of Michigan, FAA, ISO, and NASA; cofounded the AI-in-Flight working group and contributed to integrating AI into NASA's Software Engineering Handbook.
- Applied AI & data systems (2025 — present) — Architected Cortex, an enterprise data platform with integrated LLM capabilities (chat-based CRUD, inline suggestions, data extraction) for Intuitive Machines — Barrios-internal client work built on Django/EAV/MySQL, deployed internally at Barrios for dev/test validation and delivered to customer-readiness stage. Currently designing the EHP Program Hub Dashboard at NASA JSC, cataloging 100+ tools across 9 offices for the Extravehicular Activity and Human Surface Mobility Program, with planned extension to CLDP, ISS, and other programs.
- Earlier NASA work (2023 — 2024) — Evaluated Flight Test Objectives across 18 Orion subsystems toward the Artemis I Flight Analysis Report; traced SSP 51074 CERD requirements for Axiom Space docking integration with ISS through SRR.
Research Assistant, Meyerhoff Group
Electroanalytical chemistry research in the Meyerhoff group, whose work centers on electrochemical and optical sensors for clinically important analytes. Developed a novel electrochemical detection method for Δ9-THC (1–20 μM) achieving 0.13 μM limit of detection with R² = 0.995 — the third-lowest detection limit among comparable screen-printed carbon electrode devices. Published peer-reviewed findings demonstrating viability for in-field saliva testing.
Industry Experience
Engineering Specialist
Launched and led a 20-person eMotor materials laboratory on 24/7 shift coverage, testing stators and rotors at hundreds of parts per week for F-150 Lightning and Maverick Hybrid programs — full lab buildup through ISO certification and production readiness. Self-initiated a PyTorch computer vision pipeline (regression with Kalman-filtered segmentation) for varnish surface area measurement on eMotor stators, achieving 99% accuracy and saving 1+ hour per quality check.
Materials & Chemical Technician
Metallurgical analysis, material validation, and quality testing across automotive OEMs and suppliers. Established the metallurgical lab supporting the 2017 SGE crankshaft line at GM Flint Engine; led supplier corrective actions and material validation at Hyundai-Kia.
Publications
Research Projects
AI Requirements Framework
Open standard for certifying AI in safety-critical systems, spanning thirteen requirement areas (AI-1 through AI-13): operational and data foundations, AI bias, ML test coverage, continuous validation, hallucination prevention, out-of-distribution detection, adversarial robustness, explainability, human-AI teaming, privacy and data protection, multi-model systems, neural network requirements, and continuous learning and adaptation. Gap analysis confirmed no existing standard adequately addresses AI-specific failure modes in human spaceflight.
Privacy-Preserving Cognitive Twin
Adversarial evaluation pipeline studying model vulnerability to membership inference attacks: confidence-based, label-only, and shadow model variants. Benchmarked three defense strategies (DP-SGD via Opacus, Model Confidence Exclusion, and a fusion defense), sweeping ε values to empirically map privacy vs. utility tradeoffs.
μStruct — AI Microstructure Identification
Ensemble classifier combining CLIP zero-shot inference with FAISS similarity-weighted voting across 6,000+ micrographs and 16 steel microstructural phases. Productionized with FastAPI and Docker on Hugging Face Spaces.
Disaster Infrastructure Damage Recognition
CNN-based binary classifier for infrastructure damage assessment in post-event aerial and ground imagery. Empirical evaluation of classification confidence thresholds for first-responder deployment readiness.
Education
M.S. Artificial Intelligence
B.S. Mechanical Engineering
Awards
Beyond the Code
Working on machines that behave human pulled me into the philosophy of being one. I see us as spiritual creatures with intellect, manifest physically. Conscience is the first-person experience of that. Love is the quiet recognition upon being shared. Faith in something of value sets the roots, and belief and morals branch from there.
I train every day, produce music, and still want to be an astronaut. I don't have social media. I'd rather be known for what I build and how I treat people than for a curated presence.