Perception
Systems that interpret real environments: object and defect detection, pose estimation, depth and LiDAR sensing, and tracking that stays registered to physical hardware.
AI-Powered Engine Quality ControlAI AGENTS · COMPUTER VISION · SPATIAL COMPUTING
I combine AI agents, computer vision, spatial computing, and sensor data to create working tools for complex real-world workflows.
CH.01
Headset experiences that guide people through physical tasks.
Medical training
Mixed reality training app that walks medical students through total knee replacement in 3D, adapting to their decisions in real time.
10 min saved per training session
Case studyAerospace maintenance training
Gaze-driven HoloLens 2 trainer for F-18 maintenance procedures, with an agent pipeline that flags struggling trainees to supervisors.
+15% task accuracy / -20% training time
Case studyAerospace maintenance
XR inspection system that spots panel defects in real time and auto-generates the maintenance documentation that normally follows by hand.
-25% documentation overhead
Case studyAutomotive XR
Full pipeline for turning a physical engine bay into a tracked XR experience: scan, optimize, register, and overlay on the real hardware.
Case studyIndustrial IoT
HoloLens 2 app that pairs a virtual drone with live MQTT sensor streams, showing environmental telemetry beside the physical asset.
Case studyCH.02
Real-time detection that gives operators an instant verdict.
Manufacturing
Real-time inspection system that catches missing and incorrectly installed engine components on the assembly line before they ship.
Case studyManufacturing
Real-time defect detector that marks each spark plug pass or fail on screen, giving line operators an instant verdict.
Case studySmart city
Vehicle counting and flow analysis that tracks traffic through configurable detection zones to surface density patterns in real time.
Case studyCH.03
Perception taken outdoors, and the data that trains it.
Agriculture
Non-contact cattle measurement on an iPad, combining pose estimation with LiDAR depth to compute real-world body dimensions.
Case studyML tooling
Synthetic training data generated in Unity Perception to build labelled computer-vision datasets without hand-annotating real footage.
Case studyAbout
I build multimodal AI, computer-vision, XR, mobile, and edge systems that connect digital intelligence to physical work. Most of my projects start with someone doing a difficult job by hand - inspecting an aircraft panel, rehearsing a surgical procedure, measuring livestock - and end with a working system that sees the task, reasons about it, and produces something useful. I design and build the full stack: model, interaction, application, device integration, and deployment.
Systems that interpret real environments: object and defect detection, pose estimation, depth and LiDAR sensing, and tracking that stays registered to physical hardware.
AI-Powered Engine Quality ControlMulti-agent pipelines that turn sensor and process data into decisions: tool use, retrieval over technical documentation, and automated reporting into live systems.
AI-Powered Aircraft InspectionHeadset and mobile experiences that guide people through physical tasks: gaze and hand interaction, 3D scanning, spatial registration, and in-world data visualisation.
HoloLens 2 Knee Replacement Surgery GuideThe rest of the system: application code, APIs, databases, edge deployment on constrained hardware, and the integration work that makes a prototype usable.
Mixed Reality Naval Maintenance TrainerContact
If you are building something that needs to see, reason, or interact with the physical world, I would like to hear about it.
Irvine, California