Marko Järvenpää
D.Sc. (Tech.), Postdoctoral research fellow University of Oslo
 Department of Biostatistics
 Sognsvannsveien 9, Domus Medica, 0372 Oslo, Norway
 Email: m.j.jarvenpaa [at] medisin.uio.no
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About me
I am a postdoctoral research fellow at Department of Biostatistics, University of Oslo working with Prof. Jukka Corander.
I was a doctoral student in the Probabilistic Machine Learning and Machine Learning for Health research groups at Aalto University, Finland in 20162020. My advisors were Prof. Pekka Marttinen and Prof. Aki Vehtari.
In my doctoral thesis I developed a probabilistic, sampleefficient framework for ABC inference with expensive simulatorbased 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 then also 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 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 inference (especially Approximate Bayesian computation, also known as likelihoodfree or simulationbased inference), numerical methods based on Gaussian process surrogate modelling, probabilistic models and inference algorithms for computational biology applications.
Working papers
 Järvenpää, M., Corander, J. (2021). Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC. Arxiv preprint
Publications

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):147178.
Online
Arxiv preprint
(Note that the previous version of this paper was titled Parallel Gaussian process surrogate method to accelerate likelihoodfree 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 mineralassociated 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 withinhost 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 modelbased approximate Bayesian computation. Bayesian Analysis, 14(2):595622. 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 dataintensive generator of morphological tree clones. GigaScience 6(10):113.
 Potapov, I., Järvenpää, M., Åkerblom, M., Raumonen, P., Kaasalainen, M. (2015). Databased 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):4773.
Workshop papers
 Järvenpää, M., Vehtari, A., and Marttinen, P. (2019). Batch simulations and uncertainty quantification in Gaussian process surrogatebased approximate Bayesian computation. 2nd Symposium on Advances in Approximate Bayesian Inference. 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 withinhost variation. The 2019 ICML Workshop on Computational Biology. 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. (2018). A Bayesian model of acquisition and clearance of bacterial colonization. ML4Health: Machine Learning for Health NeurIPS 2018 workshop. Online
 Järvenpää, M., Gutmann, M.U., Pleska, A., Vehtari, A., and Marttinen, P. (2017). Efficient acquisition rules for modelbased approximate Bayesian computation. Advances in Approximate Bayesian Inference NIPS 2017 Workshop. Online
 Järvenpää, M., Gutmann, M.U., Vehtari, A., and Marttinen, P. (2017). Gaussian process modeling in approximate Bayesian computation to estimate horizontal gene transfer in bacteria. NIPS 2017 Workshop on Machine Learning in Computational Biology. Workshop webpage
 Lintusaari, J., Vuollekoski, H., Kangasrääsiö, A., Skytén, K., Järvenpää, M., Gutmann, M., Vehtari, A., Corander, J., and Kaski, S. (2017). ELFI: Engine for Likelihood Free Inference. ICML 2017 Workshop on Implicit Models. Online
 Kangasrääsiö, A., Lintusaari, J., Skytén, K., Järvenpää, M., Vuollekoski, H., Gutmann, M., Vehtari, A., Corander, J., and Kaski, S. (2016). ELFI: Engine for Likelihood Free Inference. Advances in Approximate Bayesian Inference NIPS 2016 Workshop. Online
Doctoral dissertation
Community involvement
Teaching (at Aalto University)
 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 in computer science.
Last modified: 9 April 2021