Aki Vehtari

ELLIS Institute

Academy Professor

T313 Dept. Computer Science

I work on the integration of computer science and Bayesian statistics, making fundamental contributions Bayesian workflow, probabilistic programming, inference methods such as Laplace, EP, VB, MC, inference and model diagnostics, model assessment and selection, Gaussian processes, and hierarchical models. I'm the leader of the Bayesian workflow group.

Full researcher profile

https://research.aalto.fi/...

aki.vehtari@aalto.fi

Phone number

+358405333747

Areas of expertise

Bayesian computational modeling, Bayesian workflow

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

Aki Vehtari

Areas of expertise

Honors and awards

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

Research groups

Publications

Raw signal segmentation for estimating RNA modification from Nanopore direct RNA sequencing data

Cross-Validatory Model Selection for Bayesian Autoregressions with Exogenous Regressors

Active Learning of Molecular Data for Task-Specific Objectives

The ARR2 Prior: Flexible Predictive Prior Definition for Bayesian Auto-Regressions

posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms

Advances in projection predictive inference

Health octo tool matches personalized health with rate of aging

Uncertainty in Bayesian Leave-One-Out Cross-Validation Based Model Comparison

The Piranha Problem: Large Effects Swimming in a Small Pond

Bayesian cross-validation by parallel Markov chain Monte Carlo