Aki Vehtari

Aki Vehtari

Associate Professor
Associate Professor
T313 Dept. Computer Science

I'm co-leader of the Probabilistic Machine Learning Group at Aalto. We develop new methods for probabilistic modeling, Bayesian inference and machine learning. Our current focuses are in particular probabilistic programming, learning from multiple data sources, Bayesian model assessment and selection, approximate inference and information visualization.

I'm member of development teams of Stan and ArviZ

Full researcher profile
https://research.aalto.fi/...

Contact information

Postal address
Konemiehentie 2 02150 Espoo Finland
Phone number
+358405333747

Areas of expertise

Bayesian modeling, Statistical analysis, Epidemiology, Brain signal analysis, Machine learning

Honors and awards

2016 De Groot Prize

The DeGroot Prize, in honor of Morris H ("Morrie" DeGroot, is awarded to the author or authors of an outstanding published book in Statistical Science
Award or honor granted for a specific work Department of Computer Science Jun 2016

Member of the winning team (Särkkä, Vehtari & Lampinen) in Time Series Prediction Competition - The CATS Benchmark 2004

Invitation or ranking in competition Department of Computer Science Jan 2004

Youden Award in Interlaboratory Testing from the American Statistical Association

Youden Award in Interlaboratory Testing from the American Statistical Association awarded to paper Sebastian Weber, Andrew Gelman, Daniel Lee, Michael Betancourt, Aki Vehtari, and Amy Racine-Poon (2018). Bayesian aggregation of average data: An application in drug development. Annals of Applied Statistics, 12(3):1583-1604.
Award or honor granted for a specific work Department of Computer Science Jan 2020

Publications

Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming

Gabriel Riutort-Mayol, Paul Christian Bürkner, Michael R. Andersen, Arno Solin, Aki Vehtari 2023 STATISTICS AND COMPUTING

Projection Predictive Inference for Generalized Linear and Additive Multilevel Models

Alejandro Catalina, Paul Burkner, Aki Vehtari 2022 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 151

Fast Methods for Posterior Inference of Two-Group Normal-Normal Models

Philip Greengard, Jeremy Hoskins, Charles C. Margossian, Jonah Gabry, Andrew Gelman, Aki Vehtari 2022 Bayesian Analysis

Atlas of type 2 dopamine receptors in the human brain: Age and sex dependent variability in a large PET cohort

Tuulia Malén, Tomi Karjalainen, Janne Isojärvi, Aki Vehtari, Paul Christian Bürkner, Vesa Putkinen, Valtteri Kaasinen, Jarmo Hietala, Pirjo Nuutila, Juha Rinne, Lauri Nummenmaa 2022 NeuroImage

Using reference models in variable selection

Federico Pavone, Juho Piironen, Paul Christian Bürkner, Aki Vehtari 2022 Computational Statistics

Feature Collapsing for Gaussian process variable ranking

Isaac Sebenius, Topi Paananen, Aki Vehtari 2022 Proceedings of The 25th International Conference on Artificial Intelligence and Statistics

Unbiased estimator for the variance of the leave-one-out cross-validation estimator for a Bayesian normal model with fixed variance

Tuomas Sivula, Måns Magnusson, Aki Vehtari 2022 COMMUNICATIONS IN STATISTICS: THEORY AND METHODS

Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison

Teemu Säilynoja, Paul Christian Bürkner, Aki Vehtari 2022 STATISTICS AND COMPUTING

Bayesian Hierarchical Stacking: Some Models Are (Somewhere) Useful

Yuling Yao, Gregor Pirš, Aki Vehtari, Andrew Gelman 2022 Bayesian Analysis