Aki Vehtari

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/...

E-post

[email protected]

Telefonnummer

+358405333747

Kompetensområde

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

Utmärkelser

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

Forskningsgrupp

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

Aki Vehtari

Kompetensområde

Utmärkelser

Youden Award in Interlaboratory Testing from the American Statistical Association

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

2016 De Groot Prize

Forskningsgrupp

Publikationer

Bayesian cross-validation by parallel Markov chain Monte Carlo

Detecting and diagnosing prior and likelihood sensitivity with power-scaling

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

Efficient estimation and correction of selection-induced bias with order statistics

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

Past, Present and Future of Software for Bayesian Inference

Pareto Smoothed Importance Sampling

Projection predictive variable selection for discrete response families with finite support

A Framework for Improving the Reliability of Black-box Variational Inference

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