Dr Luigi Palla
Medical Statistics and Epidemiology
I obtained a degree in Statistical and Economic Sciences (2000) and Ph.D. in Statistical Methodology for Scientific Research from the University of Bologna (2004). After a postdoctoral appointment in statistical/mathematical genetics at Dept Mathematics, Free University Amsterdam, for several years I worked as a statistician for the Medical Research Council at University of Cambridge, in the areas of genetic epidemiology (mainly supporting InterAct- a large European study of the effect of interaction between genes and lifestyle on the incidence of diabetes) and nutrition (dietary surveys, nutritional epidemiology). I joined LSHTM in August 2013 as Research Fellow in Statistical Genetics. Since 2016 I have been Assistant Professor in Medical Statistics and Epidemiology at the Department of Medical Statistics and member of the Electronic Health Record Research Group at LSHTM and Associate member of the Farr Institute for Health Informatics at UCL.
I am currently Director of the LSHTM DL Postgraduate Professional Development Course in Pharmacoepidemiology and Pharmacovigilance. I also co-organise a module on Advanced Statistics for Records Research in the Master in Data Science for Research in Health and Biomedicine at the Farr Institute of Health Informatics (UCL).
Between 2014 and 2016 I was organiser of the module in Analysis of Hierarchical and Other Depedent Data in the Master in Medical Statistics (LSHTM) and I am still teaching and a tutor in that module/Master. In the School I contributed to teaching the courses in Human Genetic Epidemiology and STEPH for the Master in Epidemiology; Robust Statistical Methods, Statistical Methods in Epidemiology and Survival Analysis for the Master in Medical Statistics.
In the year 2016/2017 (second semester) I was appointed Visiting Professor in Statistics at the University of Parma where I taught a course on Statistics for Food and Nutrition Sciences.
I am interested in statistical methods in epidemiology and genetics and in statistical applications of multivariate methods in metabolomics, nutrition and electronic health record (Big Data) research.