Aki Vehtari

Professor (Associate Professor), Professorship Vehtari A.
Professor (Associate Professor), Helsinki Institute for Information Technology HIIT
Professor (Associate Professor), Probabilistic Machine Learning
Professor (Associate Professor), Department of Computer Science

https://orcid.org/0000-0003-2164-9469

Phone+358405333747
Emailaki.vehtari@aalto.fi

Aalto SCI Computer Science Konemiehentie 2

Research output per year

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Personal profile

Artistic and research interests

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. Our primary application areas are digital health and biology, neuroscience and user interaction.

I'm visiting professor at Technical University of Denmark (DTU), and I have a shared office at Columbia University in the City of New York. I'm member of development teams of Stan, ELFI, GPy and GPstuff.

Education/Academic qualification

Doctor of Science (Technology), Engineering Physics

Master of Science (Technology), Information Technology

Keywords

Bayesian modeling
Statistical analysis
Epidemiology
Brain signal analysis
Machine learning

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Research Output

81 Article
51 Conference contribution
18 Working paper
5 Chapter
13 More

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

Järvenpää, M., Vehtari, A. & Marttinen, P., 2020, (Accepted/In press) Conference on Uncertainty in Artificial Intelligence (UAI 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data

Vehtari, A., Gelman, A., Sivula, T., Jylanki, P., Tran, D., Sahai, S., Blomstedt, P., Cunningham, J. P., Schiminovich, D. & Robert, C. P., 2020, In : Journal of Machine Learning Research. 21, p. 1-53 53 p.

Research output: Contribution to journal › Article › Scientific › peer-review

Open Access

File

55 Downloads (Pure)

Interindividual variability and lateralization of μ-opioid receptors in the human brain: Individual differences in the μ-opioid receptor system

Kantonen, T., Karjalainen, T., Isojärvi, J., Nuutila, P., Tuisku, J., Rinne, J., Hietala, J., Kaasinen, V., Kalliokoski, K., Scheinin, H., Hirvonen, J., Vehtari, A. & Nummenmaa, L., 15 Aug 2020, In : NeuroImage. 217, 8 p., 116922.

Research output: Contribution to journal › Article › Scientific › peer-review

Open Access

File