Ph.D. researcher building machine learning that runs on drones, edge devices and GPUs — under real time budgets.
Raw sensor input arrives unstructured, noisy, and far larger than the device that has to process it.
Models learn the shape hidden inside it — while fitting into memory, power and latency budgets that leave no room to spare.
What comes out the other side is small enough, fast enough, and reliable enough to run where it actually matters.
How much intelligence can you fit into a device that has almost nothing to spare?
Detection frameworks designed for drone-mounted deployment, where compute, memory and power are all tightly bounded.
Moving search and recovery workloads off the CPU and onto thousands of CUDA cores.
Adaptive enhancement for imagery that breaks ordinary pipelines — underwater colour attenuation, turbidity, aerial scale.
Semantic tokenization and deterministic transport, plus earlier work on blockchain wallet infrastructure.
Five projects across vision, acceleration, transport and cryptography.
Pulled live from GitHub each time this page loads.
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