2026 Aki Vehtari: Mathematics Researcher – H-Index, Publications & Awards

D-Index & Metrics

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	53	4668	4538	31	28	233	60851
Mathematics	55	757	647	2	1	230	62230

Discipline name

D-Index

World Ranking

Current World Ranking

National Ranking

Current National Ranking

Publications

Citations

Computer Science

4668

4538

233

60851

Mathematics

757

647

230

62230

Computer Science

D-Index

Citations

60851

World Ranking

4668

National Ranking

Mathematics

D-Index

Citations

62230

World Ranking

757

National Ranking

Research.com Recognitions

2026 - Research.com Mathematics in Finland Leader Award
2025 - Research.com Mathematics in Finland Leader Award

Overview

Aki Vehtari is affiliated with Aalto University in Finland and has contributed extensively to the fields of mathematics and computer science. Their research predominantly spans statistics and probability, with a focus on statistical methods and Bayesian inference.

The scientist's work covers several subfields, including:

Statistics and Probability
Artificial Intelligence
Management Science and Operations Research
Statistics, Probability and Uncertainty
Molecular Biology

Main topics in Aki Vehtari's research include:

Statistical Methods and Inference
Statistical Methods and Bayesian Inference
Gaussian Processes and Bayesian Inference
Bayesian Methods and Mixture Models
Markov Chains and Monte Carlo Methods
Bayesian Modeling and Causal Inference
Advanced Multi-Objective Optimization Algorithms

Vehtari has a record of numerous publications across prominent venues, including the following frequent publication venues:

arXiv (Cornell University)
Bayesian Analysis
Statistics and Computing
Computational Statistics
bioRxiv (Cold Spring Harbor Laboratory)

Among recent notable papers are:

Bayesian Workflow (2020), published in arXiv (Cornell University)
Interindividual variability and lateralization of μ-opioid receptors in the human brain (2020), published in NeuroImage
Projective inference in high-dimensional problems: Prediction and feature selection (2020), published in Electronic Journal of Statistics
Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison (2020), published in arXiv (Cornell University)
What are the Most Important Statistical Ideas of the Past 50 Years? (2021), published in Journal of the American Statistical Association

Aki Vehtari frequently collaborates with several coauthors, including:

Andrew Gelman
Jennifer Hill
Paul-Christian Bürkner
Måns Magnusson
Michael Riis Andersen

In addition to articles, Vehtari has published books with Cambridge University Press, including:

Regression and Other Stories (2020)
Active Statistics (2024)

Best Publications

Bayesian Data Analysis

Andrew Gelman;John B. Carlin;Hal S. Stern;David B. Dunson

32466
Citations
Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC

Aki Vehtari;Andrew Gelman;Jonah Gabry

5355
Citations
Understanding predictive information criteria for Bayesian models

Andrew Gelman;Jessica Hwang;Aki Vehtari

2312
Citations
One vs three years of adjuvant imatinib for operable gastrointestinal stromal tumor: a randomized trial.

Heikki Joensuu;Mikael Eriksson;Kirsten Sundby Hall;Jörg T. Hartmann

1192
Citations
Visualization in Bayesian workflow

Jonah Gabry;Daniel Simpson;Aki Vehtari;Michael Betancourt

1066
Citations
R-squared for Bayesian regression models

Andrew Gelman;Ben Goodrich;Jonah Gabry;Aki Vehtari

874
Citations
Regression and Other Stories

Andrew Gelman;Jennifer Hill;Aki Vehtari

570
Citations
Discussion to "Bayesian measures of model complexity and fit" by Spiegelhalter, D.J., Best, N.G., Carlin, B.P., and van der Linde, A.

Aki Vehtari

516
Citations
Discussion of "Using Stacking to Average Bayesian Predictive Distributions" by Yao et. al

Yuling Yao;Aki Vehtari;Daniel Simpson;Andrew Gelman

484
Citations
Sparsity information and regularization in the horseshoe and other shrinkage priors

Juho Piironen;Aki Vehtari

476
Citations
Bayesian approach for neural networks—review and case studies

Jouko Lampinen;Aki Vehtari

461
Citations
A survey of Bayesian predictive methods for model assessment, selection and comparison

Aki Vehtari;Janne Ojanen

409
Citations
Rao-Blackwellized particle filter for multiple target tracking

Simo Särkkä;Aki Vehtari;Jouko Lampinen

378
Citations
Comparison of Bayesian predictive methods for model selection

Juho Piironen;Aki Vehtari

370
Citations
GPstuff: Bayesian modeling with Gaussian processes

Jarno Vanhatalo;Jaakko Riihimäki;Jouni Hartikainen;Pasi Jylänki

345
Citations
Using stacking to average Bayesian predictive distributions

Yuling Yao;Aki Vehtari;Daniel Simpson;Andrew Gelman

283
Citations
Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra.

Kunal Ghosh;Annika Stuke;Milica Todorović;Peter Bjørn Jørgensen

256
Citations
Pareto Smoothed Importance Sampling

Aki Vehtari;Andrew Gelman;Jonah Gabry

256
Citations
Bayesian model assessment and comparison using cross-validation predictive densities

Aki Vehtari;Jouko Lampinen

254
Citations
Validating Bayesian Inference Algorithms with Simulation-Based Calibration

Sean Talts;Michael Betancourt;Daniel Simpson;Aki Vehtari

182
Citations
Hierarchical linear models

Andrew Gelman;John B. Carlin;Hal S. Stern;David B. Dunson

41
Citations
Bayesian Workflow.

Andrew Gelman;Aki Vehtari;Daniel Simpson;Charles C. Margossian

15
Citations

Frequent Co-Authors

Andrew Gelman Columbia University

Donald B. Rubin Temple University

John B. Carlin University of Melbourne

Simo Särkkä Aalto University

David B. Dunson Duke University

Samuel Kaski Aalto University

Mikko Sams Aalto University

Heikki Joensuu University of Helsinki

Matti Hämäläinen Harvard Medical School

Iiro P. Jääskeläinen Aalto University

External Links

Personal website of Aki Vehtari Google Scholar page

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