Research output per year
Research output per year
Accepting PhD Students
We are interested in applications of pure mathematics and mathematical statistics to causal questions in population biomedicine and public health policy. We take a cross-disciplinary approach, collaborating closely with experts from diverse backgrounds. Where possible, we develop and take advantage of model-independent methods in mathematical statistics and machine learning, such as Targeted Learning.
Targeted Learning allows for the construction of estimators of biological quantities that can be mathematically proven to have an optimal bias-variance trade-off. This is essential in light of the arrival of truly large-scale databases, such as the UK Biobank, as size presents novel challenges to current statistical techniques: ever more precise measurements (smaller variance) expose untrue biological or modelling assumptions (bias). Since it is rarely possible to quantify bias a posteriori, we employ deep mathematical theory to obtain a priori control over bias.
Currently, our research is focussed on two contexts:
Mathematics, Doctor of Philosophy (PhD), School of Mathematics, University of Edinburgh
1 Sep 2014 → 31 Aug 2018
Award Date: 29 Nov 2018
Postdoctoral Research Fellow, Rheinische Friedrich-Wilhelms-Universität Bonn
2 Sep 2019 → 1 Sep 2020Research output: Contribution to journal › Article › peer-review
Research output: Contribution to journal › Article › peer-review
Research output: Working paper
Research output: Working paper