Mattias Villani is Professor of Statistics and Head of the Division for Statistics and Machine Learning (STIMA) in the Department of Computer and Information Science (IDA) at Linkoping University (LiU) in Linköping, Sweden.

He received his Ph.D. from Stockholm University in 2000. He joined LiU in June 2011, following positions as Senior Lecturer in Statistics at Stockholm University, and Researcher and Advisor at Sveriges Riksbank (Central Bank of Sweden). He has been Senior Visiting Scholar at University of New South Wales in 2009 and 2011.

His main professional interests are currently at the intersection of Statistics and Machine Learning. His research focuses on developing computationally efficient Bayesian methods for inference, prediction and decision making using flexible probabilistic models. Current application areas include neuroimaging, text analysis, econometrics and robotics.


Teaching (current)

Bayesian Learning (TDDE07), master and Phd level (Spring semester)



Google scholar profile

External web page with publications

Current research funding: VR (Swedish Research Council, 2014-2017); SSF (Swedish Foundation for Strategic Research) (2017-2021)


Ph.D. Students (main supervisor):

Josef Wilzén (Statistics) – Topic: Bayesian Analysis of Neuroimaging Data

Per Sidén (Statistics) – Topic: Spatial Models in Neuroimaging

Hector Rodriguez-Deniz (Statistics) – Topic: Bayesian Learning for Spatio-Temporal Models in Transportation.

Ph.D. Students (co-supervisor):

Olov Andersson (Machine Learning/CS) – Topic: Machine Learning for Robotics.

Johan Falkenjack – (Machine Learning/CS)- Topic: Natural Language Processing. Readability of texts.

Sarah Alsaadi (Statistics) – Topic: Psychometrics. Multi-level models.

Munezero Parfait (Statistics, SU) – Topic: Bayesian survival analysis.

Caroline Svahn (Statistics) – Topic: Machine Learning for 5G System – Control and Automation.

Ph.D. Graduates (main supervisor):

Måns Magnusson – Thesis: Scalable and Efficient Probabilistic Topic Model Inference for Textual Data.  PhD in Statistics, Linköping University, 2018.
Currently Post Doc at Aalto University.

Matias Quiroz – Thesis: Bayesian Inference for Large Data Problems. Ph.D. in Statistics, Stockholm University, 2015.
Awarded a Wallander Post Doc scholarship
Currently post doc in Statistics at UNSW, Sydney.

Feng Li – Thesis: Bayesian Modeling of Conditional Densities. Ph.D. in Statistics, Stockholm University, 2013.
Awarded the 2014 Cramér prize for best PhD thesis in Statistics and Mathematical Statistics.
Currently Assistant Professor, Central University of Finance and Economics, Beijing, China.

Bertil Wegmann – Thesis: Bayesian Inference in Structural Second-Price Auctions. Ph.D. in Statistics, Stockholm University, 2011.
warded a Wallander Post Doc scholarship
Currently Assistant Professor at Division of Statistics and Machine Learning, Linköping University.

Ph.D. Graduates (co-supervisor)

Roy Chandran (Machine Learning/CS) – Topic: Neural Networks for Hurricane Prediction.

Christian Tallberg – Thesis: Bayesian and Other Approaches for Analyzing Network Block-Structures (joint with Ove Frank). Ph.D. in Statistics, Stockholm University, 2003.

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