Marko Järvenpää

D.Sc. (Tech.), Postdoctoral research fellow

Sognsvannsveien 9, Domus Medica, 0372 Oslo, Norway

Email: m.j.jarvenpaa [at] medisin.uio.no

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About me

I have worked in the reseach group of Prof. Jukka Corander at Department of Biostatistics, University of Oslo since 2020. From mid-2022 to mid-2023 or so I suffered from Long COVID and worked only with a very limited capacity but have now luckily recovered.

I was a doctoral student in the Probabilistic Machine Learning and Machine Learning for Health research groups at Aalto University, Finland in 2016-2020. My advisors were Prof. Pekka Marttinen and Prof. Aki Vehtari. I was a visiting researcher at Harvard T.H. Chan School of Public Health during the autumn 2017. I obtained M.Sc. degree in applied mathematics from Tampere University of Technology, Finland in 2013.

Research interests

Approximate Bayesian computation (ABC) and other "likelihood-free" Bayesian methods.
Sample-efficient approximate Bayesian inference using Gaussian processes and active learning/sequential experimental design.
Generalized Bayesian inference and predictive inference.

Papers

Järvenpää, M., Corander, J. (2023). Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC. Submitted. 55 pages. Arxiv preprint
Järvenpää, M., Corander, J. (2023). On predictive inference for intractable models via approximate Bayesian computation. Statistics and Computing, 33 (2), 42. Online Arxiv preprint
Järvenpää, M., Gutmann, M.U., Vehtari, A., and Marttinen, P. (2021). Parallel Gaussian process surrogate Bayesian inference with noisy likelihood evaluations. Bayesian Analysis, 16(1):147-178. Online Arxiv preprint
(Note that the previous version of this paper was titled Parallel Gaussian process surrogate method to accelerate likelihood-free inference.)
Järvenpää, M., Vehtari, A., and Marttinen, P. (2020). Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. In Proceedings of Conference on Uncertainty in Artificial Intelligence (UAI 2020). Online Arxiv preprint Poster
Karhu, K., Hilasvuori, E., Järvenpää, M., Arppe, L., Christensen, B.T., Fritze, H., Kulmala, L., Oinonen, M., Pitkänen, J.-M., Vanhala, P., Heinonsalo, J., and Liski J. (2019). Similar temperature sensitivity of soil mineral-associated organic carbon regardless of age. Soil Biology and Biochemistry, 136:107527. Online
Järvenpää, M., Sater, M.R.A., Lagoudas, K.G., Blainey, P.C., Miller, L.G., McKinnell, J.A., Huang, S.S., Grad, Y.H. and Marttinen P. (2019). A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation. PLoS Computational Biology, 15(4):e1006534. Online
Järvenpää, M., Gutmann, M.U., Pleska, A., Vehtari, A., and Marttinen, P. (2019). Efficient acquisition rules for model-based approximate Bayesian computation. Bayesian Analysis, 14(2):595-622. Online Arxiv preprint
Lintusaari, J., Vuollekoski, H., Kangasrääsiö, A., Skytén, K., Järvenpää, M., Marttinen, P., Gutmann, M., Vehtari, A., Corander, J., and Kaski, S. (2018). ELFI: Engine for Likelihood Free Inference. Journal of Machine Learning Research 19(16):1−7. Online Arxiv preprint
Järvenpää, M., Gutmann, M.U., Vehtari, A., and Marttinen, P. (2018). Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria. Annals of Applied Statistics 12(4):2228–2251. Online Arxiv preprint
Potapov, I., Järvenpää, M., Åkerblom, M., Raumonen, P., Kaasalainen, M. (2017). Bayes Forest: A data-intensive generator of morphological tree clones. GigaScience 6(10):1-13.
Potapov, I., Järvenpää, M., Åkerblom, M., Raumonen, P., Kaasalainen, M. (2015). Data-based stochastic modeling of tree growth and structure formation. Silva Fennica 50(1).
Piché, R., Järvenpää, M., Turunen, E., Šimůnek, M. (2014). Bayesian analysis of GUHA hypotheses. Journal of Intelligent Information Systems. 42(1):47-73.

Workshop papers

List of workshop papers.

Doctoral dissertation

In my doctoral thesis I developed a probabilistic, sample-efficient framework for ABC inference with expensive simulator-based statistical models. This inference framework is based on Gaussian process surrogate modelling and Bayesian decision theory, and could be called as "Bayesian ABC" in analogy with related probabilistic numerical methods such as Bayesian quadrature and Bayesian optimisation. I extended the framework for Bayesian inference when a limited number of possibly noisy evaluations of the likelihood function (or some approximation of it) can only be obtained due to computational constraints. I also considered some population genomics applications.
Järvenpää, M. (2020). Gaussian Process Surrogate Methods for Sample-Efficient Approximate Bayesian Computation. Department of Computer Science, Aalto University. Online

Community involvement

I have served as a reviewer for Journal of Bioinformatics and Computational Biology, Journal of Computational and Graphical Statistics, SIAM/ASA Journal on Uncertainty quantification, Statistics and Computing, AAAI (2020), AABI (Advances in approximate Bayesian inference) (2018, 2019), AISTATS (2018, 2020-2022), ICML (2019), NeurIPS (2018-2020).

I was selected as one of the top reviewers of NeurIPS 2019 and AISTATS 2022.

Teaching

Machine Learning: Advanced Probabilistic Methods, spring 2018, 2019, 2020, teaching assistant. Lectured by Prof. Pekka Marttinen.
Bayesian Data Analysis, autumn 2016, teaching assistant. Lectured by Prof. Aki Vehtari.
Supervisor of two B.Sc. theses and advisor of one M.Sc. thesis at Aalto University.