Alvaro Tejero-Cantero

My background is in theoretical physics (MSc) and computational neuroscience (PhD). I was fortunate to work with Klaus Hornberger on the theory of interference of entangled material particles for my MSc and for my PhD with Christian Leibold on models of memory sequences, combining theoretical neural-network models and data analysis on in-vitro recordings. In between I worked at the German Aerospace Agency and the European Southern Observatory.

During my first postdoc in Oxford I attended Nando de Freitas' and Phil Blunsom's lecture and grew interested in machine learning, complementing the classical signal analysis techniques with new Bayesian time-series models in work with Diego Vidaurre. After Oxford, I spent a few years between academia and industry, figuring out the right amount of theory and application, and which field in Science I liked most. It turns out I liked most of them!

My last station was with Jakob Macke, where I could work on what, for the physicist in me, is a very happy synthesis: unleashing the power of machine learning on mechanistic models in order to solve the inverse problem. So-called simulation-based inference methods aim at a Bayesian characterization of the parameters of a mechanistic model that make it reproduce actual observations.