I work as a researcher in statistical anomaly detection in live data streams. I work at Lancaster University and my research is funded by the Detection of Anomalous Structure in Streaming Settings group, which is funded by a combination of industrial funding and the Engineering and Physical Sciences Research Council (ultimately the UK Government).

There’s a very critical research problem that’s surprisingly open—if you are monitoring a noisy system for a change of state, how do you ensure that you find any change as soon as possible, while keeping your monitoring costs as low as possible?

By “low”, I really do mean low—I am interested in methods that take far less power than (for example) modern AI tools. If the computational cost of monitoring is high, the monitoring just won’t get done, and then something will go wrong and cause a lot of problems before we realise and try to fix things.

This has applications in a lot of areas and is valued by a lot of people. I work with a large number of industrial, scientific and government partners.

Improving the underlying mathematical tooling behind figuring out when complex systems start to show problems reduces existential risk. If for some reason we all die, it’ll be because something somewhere started going very wrong and we didn’t do anything about it in time. If my research has anything to say about it, “the monitoring system cost us too much power so we turned it off” won’t be on the list of reasons why that happened.

I also donate to effective global health and development interventions and support growth of the effective giving movement. I believe that a better world is eminently possible, free from things like lead pollution and neglected tropical diseases, and that everyone should be doing at least something to try to genuinely build a better world.