My name is Gregor Simm and I am a postdoctoral fellow working with José Miguel Hernández-Lobato in the Machine Learning Group at the University of Cambridge. I am interested in applying Bayesian optimization to accelerate the discovery of novel materials and chemical reactions. My research is supported by an Early Postdoc.Mobility fellowship of the Swiss National Science Foundation.
From 2015 to 2018, I was pursuing my Ph.D. in theoretical chemistry under the supervision of Markus Reiher at ETH Zurich. I developed approaches for the exploration of complex chemical reaction networks with error estimation capabilities through the application of Bayesian statistics and machine learning. My work was supported by a fellowship of the Fund of the German Chemical Industry.
I completed my BSc. and MSc. at ETH Zurich in natural sciences (Focus: Chemistry and Physics, with distinction). I carried out my master’s project in the group of Alán Aspuru-Guzik at Harvard University, where I applied Gaussian processes to predict the efficiency of organic photovoltaics.