Engineering for hardware that has to work outside the lab

Most machine learning works fine on a workstation and falls apart on a battery-powered board. I take models the rest of the way: choosing an architecture that fits the silicon, quantising it until it meets the latency and power budget, and building the inference pipeline around it so it survives contact with real hardware.

Whole-vehicle work rather than one slice of it: structures, propulsion, avionics, telemetry and autonomous behaviour, plus the fabrication to actually build the thing. I have taken vehicles from a requirement to a flying, tested prototype across electric, turbine and underwater propulsion.

Image sensor and detector work: characterisation, bench bring-up, and the data infrastructure that turns raw test output into a decision. This is my day-to-day professional practice rather than research work, and it covers the unglamorous middle of a sensor programme where most schedule risk actually lives.

FPGA design and hardware/software co-design for cases where timing, throughput or determinism rule out a general-purpose processor. Includes partial reconfiguration, where the fabric changes function at runtime rather than being fixed at build time.

A compute and communications stack that turns a swarm of 1U CubeSats into a single elastic cluster, with FPGA payloads that change function in orbit and workloads that migrate between satellites in seconds.

AQUAD: a single vehicle that flies to a site, submerges, manoeuvres underwater and returns to the air, on one charge. Built against a brief targeting ocean worlds such as Europa.

Paired resonant Tesla coils beaming power and low-frequency data across a gap with no conductor between them, and the electromagnetic compatibility engineering needed to keep nearby avionics alive.

A heavy-lift carrier UAV acting as an airborne charging hub and data relay, letting micro-drones dock in flight to recharge, offload sensor data and borrow GPU cycles.