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Maurizio Filippone

Associate Professor, Statistics

Computer, Electrical and Mathematical Science and Engineering Division
Email icon maurizio.filippone@kaust.edu.sa

Program Affiliations

Research Interests

Professor Filippone's current research interests include the development of tractable and scalable Bayesian inference techniques for Gaussian processes and Deep Learning models, with applications in life and environmental sciences.
Keyword tag icon
Bayesian deep learning computational statistics Gaussian processes

Education Profile

  • Postdoc in Statistics at UCL, UK and in Computer Science at the University of Glasgow, UK, 2011-2012

  • Postdoc in Computer Science at the University of Sheffield, UK, 2009-2010

  • PhD in Computer Science, University of Genova, Italy, 2008

  • Visiting Scholar at George Mason University, Fairfax (VA), USA, 2007

  • MSc in Physics, University of Genova, Italy, 2004

Publications

  • B.-H. Tran, G. Franzese, P. Michiardi, and M. Filippone. One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models. In NeurIPS, 2023.

  • B.-H. Tran, S. Rossi, D. Milios, and M. Filippone. All you need is a good functional prior for Bayesian deep learning. Journal of Machine Learning Research, 23(74):1--56, 2022.

  • S. Marmin and M. Filippone. Deep gaussian processes for calibration of computer models (with discussion). Bayesian Analysis, 17(4):1301-1350, 2022.

  • A. Zammit-Mangion, T. L. J. Ng, Q. Vu, and M. Filippone. Deep Compositional Spatial Models, Journal of the American Statistical Association, 117:540, 1787-1808, 2022.

  • B.-H. Tran, S. Rossi, D. Milios, P. Michiardi, E. V. Bonilla, and M. Filippone. Model selection for Bayesian autoencoders. In NeurIPS, 2021.

Research Areas