Pekka Marttinen

Postal Address:

Helsinki Institute for Information Technology HIIT, Department of Computer Science

Aalto University

P.O.Box 15400

FIN-00076 AALTO, FINLAND

Street Address:

Room A317, Konemiehentie 2, Espoo, Finland

Email:

pekka.marttinen (at) aalto.fi

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Research Group: Machine Learning for Health (Aalto-ML4H)

• Researchers

  • Pekka Marttinen, PI
  • Hans Moen, Research Fellow (jointly with the PML group)
  • Vishnu Raj, Postdoc

• PhD students

  • Tianyu Cui (jointly with PML group)
  • Yogesh Kumar
  • Shaoxiong Ji
  • Onur Poyraz
  • Caglar Hizli
  • Nicola Dainese (jointly with the Deep Learning group)
  • Antti Pöllänen
  • Joel Honkamaa
  • Fanni Ojala
  • Tommi Gröhn

• Research assistants

  • Ya Gao

• Visiting researchers

  • Prayag Tiwari
  • Shinji Hotta

• Former PhD students

  • Tomi Peltola (www), Jussi Gillberg, Marko Järvenpää (www)

• Former Postdocs

  • Reza Ashrafi, Santosh Hiremath

• Former group members (Master's students, visitors, etc)

  • Guangyi Zhang, Maximilian Proll, Erick Gijon, Alejandro Ponce de Leon Chavez, Berrenur Saylam, Severi Rissanen, Juuso Kylmäoja, Teijo Väärä, Nicola Brazzale

Research Interests

• Machine learning methodology

  • Probabilistic machine learning
  • Bayesian statistics
  • Deep learning
  • Likelihood-free inference
  • Causal inference

• Applications

  • Statistical genetics
  • Personalized medicine
  • Molecular biology
  • Electronic health records
  • Mobile health

Publications

Full list of publications

Google Scholar

Selected Articles (Probabilistic Machine Learning)

• Cui, T., Kumar, Y., Marttinen, P., and Kaski, S. (2022). Deconfounded Representation Similarity for Comparison of Neural Networks. Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), accepted. Preprint

• Rissanen, S. and Marttinen, P. (2021). A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021), pp. 4207-4217. Available online

• Cui, T., Havulinna, A., Marttinen, P.*, and Kaski, S.* (2021). Informative Bayesian Neural Network Priors for Weak Signals. Bayesian Analysis, 1-31. (*equal contribution) Advance publication

• 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. Available 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. Available online

• Gillberg, J., Marttinen, P., Pirinen, M., Kangas, A.-J., Soininen, P., Ali, M., Havulinna, A. S., Järvelin, M.-R., Ala-Korpela, M., and Kaski, S. (2016). Multiple output regression with latent noise. Journal of Machine Learning Research, 17:1-35. Available online

Selected Articles (Computational Biology)

• Marttinen, P. and Hanage, W.P. (2017). Speciation trajectories in recombining bacterial species. PLOS Computational Biology, 13(7):e1005640. Available online

• Mostowy, R., Croucher, N.J., Andam, C.P., Corander, J., Hanage, W.P., and Marttinen, P. (2017). Efficient inference of recent and ancestral recombination within bacterial populations. Molecular Biology and Evolution, 34(5):1167-1182. Available online

• Marttinen, P., Croucher, N.J., Gutmann, M.U., Corander, J. and Hanage, W.P. (2015). Recombination produces coherent bacterial species clusters in both core and accessory genomes. Microbial Genomics, 1, doi:10.1099/mgen.0.000038, Available online (Supplement)

• Marttinen, P., Pirinen, M., Sarin, A.P., Gillberg, J., Kettunen, J., Surakka, I., Kangas, A.J., Soininen, P., O’Reilly, P.F., Kaakinen, M., Kähönen, M., Lehtimäki, T., Ala-Korpela, M., Raitakari, O.T., Salomaa, V., Järvelin, M.-R., Ripatti, S. and Kaski, S. (2014). Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression. Bioinformatics, 30(14):2026-34. Available online

• Marttinen, P., Hanage, W.P., Nicholas, J.C., Connor, T.C., Harris, S.R., Bentley, S.D. and Corander, J. (2012). Detection of recombination events in bacterial genomes from large population samples. Nucleic Acids Research, 40(1): e6. Available online

• Corander, J. and Marttinen, P. (2006). Bayesian identification of admixture events using multi-locus molecular markers. Molecular Ecology, 15:2833-2843. Link

Selected Articles (Applications)

• Chewapreecha, C., Marttinen, P., Croucher, N.J.,Salter, S.J., Harris, S.R., Mather, A.E.,Hanage, W.P., Goldblatt, D., Nosten, F.H., Turner, C., Turner, P., Bentley, S.D. and Parkhill, J. (2014). Comprehensive identification of single nucleotide polymorphisms associated with beta-lactam resistance within pneumococcal mosaic genes. PLoS Genetics, 10(8):e1004547. Available online

• Kashtan, N., Roggensack, S.E., Rodrigue, S., Thompson, J.W., Biller, S.J., Coe, A., Ding, H., Marttinen, P., Malmstrom, R.R., Stocker, R., Follows, M.J., Stepanauskas, R. and Chisholm, S.W. (2014). Single-cell genomics reveals hundreds of coexisting subpopulations in wild Prochlorococcus. Science, 344(6182): 416-420. Link

• Chewapreecha, C., Harris, S.R., Croucher, N.J., Turner, C., Marttinen, P., Cheng, L., Pessia, A., Aanensen, D.M., Mather, A.E., Page, A.J., Salter, S.J., Harris, D., Nosten, F., Goldblatt, D., Corander, J., Parkhill, J., Turner, P. and Bentley, S.D. (2014). Dense genomic sampling identifies highways of pneumococcal recombination. Nature Genetics, 46: 305-309. Link

Software

I have authored/co-authored the following software:

• BAPS (Examples): Bayesian analysis of genetic population structure, see publications [1,3,4,7] in the full list
• fastGEAR: Inference of recent and ancestral recombination between divergent lineages in bacteria [34]
• BratNextGen (win32, win64, Linux): Bayesian modeling of recombination events in bacterial populations [6,14]
• SEER: A method for genome-wide association studies in bacteria [31]
• BRRR: Bayesian reduced rank regression for multivariate genome-wide association studies [22]
• Bacmeta / cog_sim / habitat_model: Simulation models for microbial population genomics [39,26,37]
• K-Pax: Bayesian clustering of protein sequences and detection of function specific residues [5]

Teaching

• Machine Learning: 2022

• Special course: Elements of Causal Inference: 2022, 2021

• Machine Learning: Advanced Probabilistic Methods: 2022, 2021, 2020, 2018, 2017, 2016, 2015

• Computational Genomics: 2020, 2019, 2018, 2017 fall, 2017 spring

• Data Science: 2019

• Information visualization: 2015

• Introduction to Bayesian Modeling: 2011, 2010

• Special Course in Bayesian Modeling (Bayesian Networks): 2010