Research
Reinforcement learning, retrieval, human robot interaction. Testing whether an idea holds up before anyone builds on it.
Four projects
How I work
The question I keep coming back to is how something decides what to do when it does not have enough information. That is my PhD, and it is also what the applied work keeps walking into from the other direction.
I try to test a claim in the situation it is supposed to hold in. Fourteen people reviewing their own lunches tell you more about a review app than any number of lab sessions, and a trust study means more when the robot is standing in the corridor while people answer.
Publishing is not the point, but it is a useful discipline. It forces the claim to survive a reader who has no reason to be generous about it, which is a harder test than any I would set myself.
ARGENTIC
My PhD. A task arrives and something has to decide how it gets handled. The research question is whether that decision can be learned rather than specified, and what it costs when it is wrong.
View project ↗Voice to Value
A month with 14 people, after which willingness to share an AI-drafted review had risen 31% and confidence in what they were about to post had risen 88%. Published at ACM MUM 2024.
View project ↗Human robot interaction
59 participants, 44 questionnaires and 17 interviews on what students and staff would need before trusting a social robot on campus. Transparency about what it does and who runs it came first. Published at ACM OzCHI 2025.
View project ↗NeuroClima
Knowledge graphs, semantic chunking and summaries in one retrieval framework, with an evaluation you can interrogate rather than take on trust. Currently being written up.
View project ↗