My research lies between Statistical Machine Learning and Signal Processing, including approximate inference, Bayesian nonparametrics, spectral estimation and computational optimal transport. I am also interested on ML/SP applications to astronomy, health, and audio. At Universidad de Chile, I have taught courses of Probability, Statistics, (Advanced) Machine Learning both for the Department of Mathematical Engineering and the Master of Data Science.

E-mail: my first initial followed by my last name (at) uchile (dot) cl


Research group

My group is called GAMES (Grupo de Aprendizaje de Máquinas, infErencia y Señales). Luckily enough, and up to a minor permutation, the acronym seems to also work in English as Group of MAchine learning, infErence and Signals. Current members are

Former members:

Short Bio

Felipe Tobar is an Associate Professor at Universidad de Chile and the Director of the Initiative for Data and Artificial Intelligence of the same Institution. He holds Researcher positions at the Center for Mathematical Modeling and the Advanced Center for Electrical and Electronic Engineering. Prior to joining Universidad de Chile, Felipe was a postdoc at the Machine Learning Group, University of Cambridge, during 2015 and he received a PhD in Signal Processing from Imperial College London in 2014. Felipe’s research interests lie in the interface between Machine Learning and Statistical Signal Processing, including approximate inference, Bayesian nonparametrics, spectral estimation, optimal transport and Gaussian processes. Felipe teaches graduate courses on Machine Learning and Statistics and between 2020 and 2022 was the Coordinator of the Master of Data Science at Universidad de Chile.

Photos: Color, Color-low-res, BW, BW-low-res