I joined the School of Mathematical and Statistical Sciences as Lecturer in Statistical Science in 2022.  I am an applied statistician working on causal inference methods for observational and experimental data, with applications across biostatistics and medical statistics. My research applications include neonatal, cardiovascular, environmental, genetic, and infectious disease epidemiology, with a focus on developing and applying methods to address real-world questions in human health. I have extensive experience collaborating with domain experts on clinical and epidemiological studies.

My main research interests are in statistical methods for the analysis of observational data. I am especially interested in statistical causal inference methods to address real-life questions about causes and effects.

Some examples of causal questions I have addressed in previous research studies: 

- does maternal alcohol consumption during pregnancy affect childrens cognitive outcomes?

- does exposure to a pollutant (perfluourooctanoic acid) in drinking water affect cholesterol levels?

- in preterm infants, does the type of feeding cause necrotising enterocolitis?

- is the association between maternal depression and childrens behaviour explained by pregnancy or post-pregnancy exposure?  

The analysis of observational data brings many challenges. There are almost always missing, poorly measured and unmeasured data. Selection of study participants can lead to biased effect estimates. Selection may also limit the generalisability of findings. Dealing with these challenges requires understanding via collaboration with experts in the specific subject area.

I have a strong interest in active learning for third-level statistics, and completed an M.Ed in University Learning and Teaching while working in the Department of Mathematics at Imperial College London (2018).