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.
Full researcher profile
https://research.aalto.fi/...
Contact information
Email
[email protected]
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
R* : A Robust MCMC Convergence Diagnostic with Uncertainty Using Decision Tree Classifiers
Ben Lambert, 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