I have a first degree in Statistics from the Athens University of Economics and Business, Greece (2001), an MSc in Statistics from the London School of Economics and Political Science (2003) and a PhD in Statistical Epidemiology from Imperial College London (2009).
Before joining the Liverpool School of Tropical Medicine I was a Lecturer in Statistics at the Institute of Psychiatry, Psychology and Neuroscience Biostatistics Department since 2013.
Prior to this appointment, I was awarded a Medical Research Council (MRC) Population Health Scientist Fellowship in 2010. This MRC fellowship has allowed me to effectively split my time between the Department of Infectious Disease at Imperial College and the Department of Statistics at the London School of Economics and Political Science-developing further my career & expanding my collaborating networks nationally and internationally.
Finally, between 2004-2010 I was the Senior Research Statistician of the Schistosomiasis Control Initiative (SCI) also based at Imperial College. This role provided me among other valuable skills with experience in study design and sample size estimations for the Monitoring & Evaluation of national Schistosomiasis Control programs. It also allowed me to get involved closely with national control staff, supporting capacity building efforts in the endemic with Schistosomiasis collaborating countries in sub-Saharan Africa; during that time I attended and successfully contributed statistical/epidemiological expertise to monitoring and evaluation as well as research meetings in Uganda, Niger, Burkina Faso, Mali, Burundi, Tanzania, Kenya, Ethiopia and Rwanda.
My methodological skills are mainly in the area of latent variable models [a broad class of models including multilevel models, latent Markov models, latent class models, latent growth curve models, structural equation models (SEMs), factor analysis, item response theory models and growth mixture models] with applications in infectious disease epidemiology and most recently in mental health epidemiology. For the analysis of Randomized Clinical Trials relevant data, SEMs can estimate direct and indirect effects and offer fully parametric modelling of more aspects of the joint distribution of variables, answering questions of “How does treatment/exposure change an outcome?” (effect mediation).