Beate Ehrhardt
My research focusses on the statistical analysis and application of machine learning to interdisciplinary research in academia, industry and the third sector. Past projects have involved statistical testing, Bayesian statistics, neural networks, modeling and statistical inference for networks, optimal experimental design, instrumental variables and causality. I have experience working with pharmaceutical data, insurance data, and data in the social sciences. Generally speaking, I am intrigued by complex and large datasets.
Before my current position, I worked as a Senior Research Statistician on the design and analysis of complex experiments to inform strategic decision making at AstraZeneca, a large pharmaceutical company. Within that role, I worked on in-vitro experiments: organs-on-chips, cell-based assays, mass spectrometry, RNA-Seq, siRNA; as well as in-vivo experiments: neurobehavioral screens and xenografts.
My background is in mathematical statistics in which I completed my PhD in 2016 at University College London (UCL) at the Department for Statistical Science on improving our understanding of community structure in large networks. Prior to my PhD, I studied mathematics at the University of Bremen, Germany, where I completed my Diploma (equivalent to Masters) in 2012.