Aki Vehtari

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
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Areas of expertise

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

Honors and awards

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

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

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

Research groups

  • Computer Science Professors, Professor
  • Computer Science - Artificial Intelligence and Machine Learning (AIML), Professor
  • Probabilistic Machine Learning, Professor
  • Professorship Vehtari Aki, Professor
  • Helsinki Institute for Information Technology (HIIT), Professor


Bayesian cross-validation by parallel Markov chain Monte Carlo

Alex Cooper, Aki Vehtari, Catherine Forbes, Dan Simpson, Lauren Kennedy 2024

Detecting and diagnosing prior and likelihood sensitivity with power-scaling

Noa Kallioinen, Topi Paananen, Paul Christian Bürkner, Aki Vehtari 2024

Modeling public opinion over time and space : Trust in state institutions in Europe, 1989-2019

Marta Kołczyńska, Paul Christian Bürkner, Lauren Kennedy, Aki Vehtari 2024

Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models

Ryoko Noda, Michael Francis Mechenich, Juha Saarinen, Aki Vehtari, Indrė Žliobaitė 2024

Past, Present and Future of Software for Bayesian Inference

Erik Štrumbelj, Alexandre Bouchard-Côté, Jukka Corander, Andrew Gelman, Håvard Rue, Lawrence Murray, Henri Pesonen, Martyn Plummer, Aki Vehtari 2024

Pareto Smoothed Importance Sampling

A Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, Jonah Gabry 2024

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

Philip Greengard, Jeremy Hoskins, Charles C. Margossian, Jonah Gabry, Andrew Gelman, Aki Vehtari 2023

Prior knowledge elicitation: The past, present, and future

Petrus Mikkola, Osvaldo Martin, Suyog Halasinamara Chandramouli, Marcelo Hartmann, Oriol Abril Pla, Owen Thomas, Henri Pesonen, Jukka Corander, Aki Vehtari, Samuel Kaski, Paul-Christian Burkner, Arto Klami 2023