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 2016-2020. My advisors were Prof. Pekka Marttinen and Prof. Aki Vehtari.

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 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 likelihood-free or simulation-based inference), numerical methods based on Gaussian process surrogate modelling, probabilistic models and inference algorithms for computational biology applications.

Working papers

  1. Järvenpää, M., Corander, J. (2021). Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC. Arxiv preprint

Publications

  1. 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.)

  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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.
  9. 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).
  10. 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

  1. Järvenpää, M., Vehtari, A., and Marttinen, P. (2019). Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation. 2nd Symposium on Advances in Approximate Bayesian Inference. Online
  2. 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. The 2019 ICML Workshop on Computational Biology. Online
  3. 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
  4. Järvenpää, M., Gutmann, M.U., Pleska, A., Vehtari, A., and Marttinen, P. (2017). Efficient acquisition rules for model-based approximate Bayesian computation. Advances in Approximate Bayesian Inference NIPS 2017 Workshop. Online
  5. 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
  6. 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
  7. 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

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 the following journals: Journal of Bioinformatics and Computational Biology, SIAM/ASA Journal on Uncertainty quantification, Statistics and Computing and for the following conferences: AAAI (2020), AABI (Advances in approximate Bayesian inference, 2018, 2019), AISTATS (2018, 2020, 2021), ICML (2019), NeurIPS (2018, 2019, 2020).
I was selected as one of the best reviewers of NeurIPS 2019.

Teaching (at Aalto University)


Last modified: 9 April 2021