Hello, I'm Andy.

I'm a PhD candidate in the Department of Data Science and Artificial Intelligence at Monash University in Melbourne, where I work on Multi-Agent Path Finding (MAPF) — figuring out how a fleet of up to thousands of robots and vehicles can move through the same environment at once without colliding, deadlocking, or wasting a second. This is exactly where I like to be: equal parts clean mathematical theory and messy real-world engineering.

My background is in Electrical and Computer Systems Engineering and Advanced Computer Science, and I spent years building things that had to actually work — flight-control algorithms for drones, a full-stack tool used by medical professionals, and more small automation projects than I can reasonably list, many of them built just to fill spare time.

I used to work as both a full-stack developer and a research assistant in a digital-health team at Monash, where I built a consultation-summary generation tool that is now deployed as part of the government's virtual-clinic system and used by medical professionals across Australia.

These days my work focuses on the practical side of MAPF, from both a computer-science theorist's perspective and a hands-on engineering one. I've written a 30-page paper just to prove half of a theorem, and I've designed robots — from the hardware up to the coordination and control algorithms — that run out on the ground.

Beyond the research

I teach across both the Master of AI and the Electrical and Computer Systems Engineering units, and I organise the international Workshop on Multi-Agent Path Finding — which means I get to spend time with the people whose papers I grew up reading. Outside the university I've been riding bikes for nearly a decade, I practise Chinese calligraphy, I keep flying drones for fun, and I share my desk with a cat who is entirely unimpressed by all of it. You can see more of that on the life page.

Always happy to talk path finding, teaching, or self-hosting.