Hello.

I am Professor of Machine Learning, in the Department of Computer Science.

What do I do?

I work on foundational and methodological aspects of Machine Learning. I enjoy looking for connections and equivalencies between known methods in the jungle of ML, primarily with tools from statistics and information theory. Everything in ML is, ultimately, a special case of something else. I find this strategy leads to: deep understanding; surprising, often beautiful insights; and, novel methods. I have also applied work, in e.g. bioinformatics, clinical trials, and predictive policing. You may like to read some details, without all the technical jargon.

I also enjoy thinking deeply about pedagogy, especially the nature of PhD training. I wrote a book - a step-by-step guide to the intellectual and emotional rollercoaster of Your PhD. Written in collaboration with twelve leading academics and industrialists, giving their unique perspectives on the PhD process, How to get Your PhD: A Handbook for the Journey is now available.

Contact me :
firstname.secondname AT manchester.ac.uk


News

See my archived news for older work, but recent activities have been…


March 2022  New paper published in AISTATS 2022: Bias-Variance Decompositions for Margin Losses with Danny Wood and Tingting Mu.

September 2021  I'm starting my research sabbatical for one full year... and I'm working on something very, very cool. Stay tuned - results expected June 2022....

June 2020  Very pleased to announce a new paper in ECML 2020... To Ensemble or Not Ensemble: When does End-To-End Training Fail?. In collaboration with many colleagues from Manchester, this is a key output from our EPSRC funded LAMBDA project, investigating the issues of modularity and cooperative training in deep neural networks.

June 2019  Very pleased to announce our new paper to be published in ECML. Joint work, sponsored by AstraZeneca, this means we can quantify uncertainty in feature selection algorithms even when we have highly interdependent features - On The Stability of Feature Selection in the Presence of Feature Correlations. The acceptance rate was 18% this year.