Noticias en español
International Research Associate
PhD in Adaptive Signal Processing, Imperial College London, UK (2014)
Master of Science in Electrical Engineering, University of Chile (2010)
Civil Engineering in Electricity, Universidad de Chile (2008)
Master of Science in Electrical Engineering, University of Chile (2010)
Civil Engineering in Electricity, Universidad de Chile (2008)
Institution: Imperial College London, UK
Academic hierarchy: Profesor Asociado
Academic hierarchy: Profesor Asociado
Works at:
CMM Data Science
CMM Data Science
Research interests:
My research lies in the intersection of Statistical Machine Learning and Signal Processing, including approximate inference, spectral estimation, optimal transport, diffusion models, and Gaussian processes. He has taught postgraduate courses on statistics, machine learning and deep generative models, and has led data science projects on conservation, health, astronomy, gender studies, and retail.
My research lies in the intersection of Statistical Machine Learning and Signal Processing, including approximate inference, spectral estimation, optimal transport, diffusion models, and Gaussian processes. He has taught postgraduate courses on statistics, machine learning and deep generative models, and has led data science projects on conservation, health, astronomy, gender studies, and retail.
f.tobar (at) imperial.ac.uk
ResearchGate Profile
Bio:
Felipe Tobar is a Senior Lecturer in Machine Learning at the Department of Mathematics and I-X at Imperial College London. Previously, he was an Associate Professor at the Initiative for Data and Artificial Intelligence, Universidad de Chile, and the Coordinator of the Master of Data Science at the same institution. He held 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 (2015), and obtained his PhD in Signal Processing from Imperial College London in 2014.
Felipe’s research lies in the intersection of Statistical Machine Learning and Signal Processing, including approximate inference, spectral estimation, optimal transport, diffusion models, and Gaussian processes. He has taught postgraduate courses on statistics, machine learning and deep generative models, and has led data science projects on conservation, health, astronomy, gender studies, and retail.
Personal HomepageFelipe Tobar is a Senior Lecturer in Machine Learning at the Department of Mathematics and I-X at Imperial College London. Previously, he was an Associate Professor at the Initiative for Data and Artificial Intelligence, Universidad de Chile, and the Coordinator of the Master of Data Science at the same institution. He held 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 (2015), and obtained his PhD in Signal Processing from Imperial College London in 2014.
Felipe’s research lies in the intersection of Statistical Machine Learning and Signal Processing, including approximate inference, spectral estimation, optimal transport, diffusion models, and Gaussian processes. He has taught postgraduate courses on statistics, machine learning and deep generative models, and has led data science projects on conservation, health, astronomy, gender studies, and retail.
ResearchGate Profile
