Andrés Guzmán-Cordero

I am first-year PhD student at Mila supervised by Kirill Neklyudov, interested in the intersection of the natural sciences, statistics, and machine learning. Currently, I build deep learning tools to make molecular simulations better, especially for electronic structure and many-body sampling.

Before my PhD, I earned an MPhil at the University of Amsterdam where I worked with Jan-Willem van de Meent on variational inference in generative models, and went on to write my thesis at the University of Toronto working with Alán Aspuru-Guzik on Bayesian and manifold optimization. Previously, I did my BSc at the Vrije Universiteit Amsterdam where I worked with Andre Lucas on statistical models for particle tracking.