Marko Järvenpää

M.Sc. (Tech.), Doctoral student
Aalto University
Helsinki Institute for Information Technology HIIT
Department of Computer Science
Probabilistic Machine Learning Group

Room: Konemiehentie 2, 3rd floor, A338
Email: marko.j.jarvenpaa [at] aalto.fi
GitHub Google Scholar

About me

I am a doctoral student under supervision of Prof. Pekka Marttinen and Prof. Aki Vehtari in the Probabilistic Machine Learning and Machine Learning for Health (Aalto-ML4H) research groups at Aalto University.

I am broadly interested in computational (Bayesian) statistics and probabilistic machine learning. My current line of research mainly consists of the two following topics:

I obtained M.Sc. degree in applied mathematics from Tampere University of Technology (TUT) in 2013. During 2012-2013 I worked (part-time) with positioning algorithms at TUT (paper 1). During 2013-2015 I was with TUT and the Finnish Environmental Institute working with data analysis for forest and soil science (papers 2-3 and 8). I have been a doctoral student at Aalto since the beginning of 2016. I was a visiting researcher at Harvard T.H. Chan School of Public Health during the autumn 2017.

Working papers

  1. Järvenpää, M., Gutmann, M.U., Vehtari, A., and Marttinen, P. (2019). Parallel Gaussian process surrogate method to accelerate likelihood-free inference. Submitted. Arxiv preprint

Publications

  1. 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
  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. PLoS Computational Biology, 15(4):e1006534. Online
  3. 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
  4. 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
  5. 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
  6. 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.
  7. 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).
  8. Piche, R., Järvenpää, M., Turunen, E., Simunek, M. (2013). Bayesian analysis of GUHA hypotheses. Journal of Intelligent Information Systems. 42(1):47-73.

Workshop papers

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

Teaching


Last modified: 2 July 2019