~5 years at NIST (2020–2025) developing standard test methods, scoring systems, and evaluation frameworks for aerial, ground, and aquatic uncrewed systems. I turn operational needs into repeatable test procedures — designing apparatus, running field and bench evaluations, and producing documentation others can reuse — with contributions to ASTM E54.09 standards development. Behind the test work sits an engineer’s software toolset: Python data pipelines, applied computer vision on edge hardware, and embedded prototyping.
Featured Projects
Selected work in robotics evaluation, test-method development, and applied software.
NIST Response Robot & UAS Test Methods
Developed and validated standard test methods for emergency-response robots at NIST (2020-2025) across aerial, ground, and aquatic domains — test apparatus design, scoring systems, and standards-oriented documentation aligned with ASTM E54.09. First author of a NIST/IEEE-published paper on aerial drone test methods for wildfire response.
RoboCup Brazil 2025 Arena Design
A competition-grade robot-test arena taken from first model to final rendered walkthrough inside a ~2-day design window: 4 CAD revisions, 27 multi-angle renders, and 2 rendered video walkthroughs.
LLM_Pipeline
A multi-stage Python pipeline that turns PDFs and video into a metadata-indexed, RAG-ready corpus: OCR-fallback text extraction, GPU-aware Whisper transcription, and token-aware chunking with SHA256 document IDs. Three implemented stages, ~590 lines across 3 scripts.
Selected Capabilities
Standard test-method development · Verification & validation · ASTM E54.09 standards development (incl. E3132/E3132M re-balloting support) · sUAS operations (FAA Part 107) · UAV fleet management & Remote ID compliance · Drop testing & confined-space evaluation · Remote evaluation frameworks & scoring · SOP/JHA & technical-report authorship · Thermal/visual acuity benchmarking · HIL/SIL/MIL workflows · Python data engineering (Whisper speech-to-text, OCR-fallback extraction, token-aware chunking) · Applied AI/CV (YOLO-OCR on NVIDIA Jetson) · Embedded systems (ESP32, Jetson) · CAD & simulation (SketchUp, Unreal Engine, JSBSim) · Tableau analytics
Published Research
Fraley, A., Nowery, E., Devine, M., Jacoff, A., Oh, P. — “Adapting NIST Aerial Drone Tests for Thermal Identification, Inspection and Suppression of Wildfires.” NIST, published December 6, 2024; presented at the 2025 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC 2025), Las Vegas, NV.
First author. Read on nist.gov
Applied Software & AI
Test engineering today runs on software, and I build my own tools: a multi-stage Python corpus pipeline (LLM_Pipeline, three implemented stages), a YOLO-OCR computer-vision pipeline on NVIDIA Jetson Orin Nano for extracting thermal readings from UAV video, a configurable drone simulation environment built on Unreal Engine and JSBSim, and wireless embedded prototypes on ESP32 hardware.
In Parallel
In parallel, I run Curiosell Systems LLC, an independent technical studio where I design, build, and ship iOS apps and developer tools with privacy-first architecture — including France Drone Prep (EU drone exam preparation), LogLens (flight-log diagnostics), and EventScope Vision (live frame-based event-style visualization).
Currently pursuing an M.S. in Data Analytics at the University of Maryland Global Campus (expected December 2027), extending test-and-evaluation experience with formal data analysis and visualization training. More on the About page
Get in Touch
Open to professional inquiries, collaboration, and project-related conversations — especially robotics testing, UAS evaluation, simulation, and aerospace engineering opportunities.