My primary research interest is in causal inference, with a specific focus on dynamic treatment regimes and personalized medicine. Dynamic treatment regimes are sequences of decision rules that take subject-level data (such as age, health status, or prior treatment) as input and recommend actions (such as which drug to take) as output. Working with longitudinal datasets, my work focuses on deriving methodologies that help identify the sequence of treatment decisions that yields the best expected outcome.
More generally, I am interested in identifying new ways to apply methods from different disciplines in new settings. This includes modifying methodology from one area of statistics so that it may be applied in a different area, or through applying statistical methods to novel problems in the 'real world' of data analysis.
I received my PhD from the London School of Hygiene and Tropical Medicine in 2012, with my thesis focusing on classical covariate measurement error. Prior to this, I received an MSc in statistics from University College London, and an MA in mathematics from Trinity College Cambridge.
From 2013-2016 I was a postdoctoral fellow at McGill University, working with Erica Moodie and David Stephens, while from 2011-2013 I worked as a statistician in a multi-disciplinary team at City University, London, studying treatment of childhood eye diseases.
For an up-to-date list of my recent publications, please see my personal website.