Research Manifesto

Cross-disciplinary research

My research sits at the intersection of artificial intelligence, scientific discovery, and next-generation computing.

Less to achieve more

The future of AI will depend not only on what we can build, but on what we can build sustainably.

My research seeks principles that allow us to achieve more with less. Less energy, less computation, and less environmental cost. Scientific progress should not be measured solely by the amount of resources consumed, but by the efficiency with which knowledge is extracted and applied.

Reliability

As AI becomes embedded in science, understanding uncertainty, failure modes, and model behavior is another dimension of evaluating performance.

Data as a scarce resource

Many of the most important scientific problems are characterised some sort of data scarcity, noise, cost, or uncertainty. Preserving provenance, attribution, integrity, and quality is essential for maintaining reliable scientific knowledge.