About Me

I'm a Research Associate at The Alan Turing Institute and primarily work in the Health Programme. My research interests are in Machine Learning for Causal Inference and Variable Selection and its diverse applications in health and medical research (including diabetes, obesity, physical activity, nutrition and pediatrics). I have worked with varied datasets recorded over randomised controlled trials and observational datasets (e.g., from wearable devices and electronic health record (EHR) databases).

I received my PhD in Statistics from the University of Glasgow in 2019 and moved to the Institute in April 2019. My thesis was titled "Nonparametric clustering for spatio-temporal data" and focused on developing novel clustering methods to account for diverse constraints introduced by sensor-device generated datasets. Previously, I also received a M.S. in Biostatistics from the University of Minnesota, Twin Cities. As a student, I worked as a Research Assistant on projects with epidemiologists at the University of Minnesota Healthy Weight Research Centre, pediatric surgeons at the Medical School and a multi-disciplinary team developing a smartphone application to track physical activity and human behaviour (now patented as Daynamica). I also received a B.Sc. in Mathematics with Economics (with First Class Honours) from the University of East Anglia and a Diploma in Economics from the University of London.

Pronouns: (She/her/hers)