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)
  • Ti John, Research Fellow
  • Minttu Alakuijala, Postdoc (jointly with the PML group)
  • Alexander Nikitin, Postdoc
  • Negar Safinianaini, Postdoc

• PhD students

  • Yogesh Kumar
  • Onur Poyraz
  • Caglar Hizli
  • Antti Pöllänen
  • Joel Honkamaa
  • Fanni Ojala
  • Tommi Gröhn
  • Ya Gao
  • Co-supervisor with Alexander Ilin in the Deep Learning group:
    • Nicola Dainese
    • Katsiaryna Haitsiukevich
    • Kalle Kujanpää
    • Sam Spilsbury
    • Arturs Polis
  • Co-supervisor with Rohit Babbar in the Extreme Classification group:
    • Erik Schultheis
    • Jinbin Zhang
    • Mohammadreza Mohammadnia Qaraei
    • Nasibullah Nasibullah

• Research assistants

  • Arina Odnoblyudova

• Visiting researchers

  • Prayag Tiwari
  • Vishnu Raj

• Former PhD students

  • Tianyu Cui (www), Tomi Peltola (www), Jussi Gillberg, Marko Järvenpää (www), Shaoxiong Ji (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, Priscilla Ong, Jasu Vehtari, Shinji Hotta

• Recruiting

  • We look for excellent candidates for PhD students and Postdocs in relevant topics. At the moment, PhD applications can be sent through an ongoing doctoral pilot call organized by FCAI, whose deadline is April 2, 2024. At other times, postdoc and PhD applications can be sent through the calls organized by HIIT. In the doctoral pilot call we are looking for candidates especially on topics related to large language and other foundation models and reinforcement learning. Other possible topics include causality, deep learning, and probabilistic machine learning.

Research Interests

• Machine learning methodology

  • Bayesian methods
  • Deep learning
  • Causal inference
  • Natural language processing
  • Reinforcement learning

• Applications

  • Bioinformatics
  • Statistical genetics
  • Epidemiology
  • Personalized medicine

Publications

Full list of publications

Google Scholar

Selected Articles (Probabilistic Machine Learning)

• Dainese, N., Marttinen, P., and Ilin, A. (2023). Reader: Model-based language-instructed reinforcement learning. The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023), accepted. Preprint

• Hizli, C., John, S.T., Juuti, A., Saarinen, T., Pietiläinen, K., and Marttinen, P. (2023). Temporal causal mediation through a point process: direct and indirect effects of healthcare interventions. Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023), accepted. Preprint

• Hizli, C., John, S.T., Juuti, A., Saarinen, T., Pietiläinen, K., and Marttinen, P. (2023). Causal Modeling of Policy Interventions From Treatment–Outcome Sequences. In Proceedings of the 40th International Conference on Machine Learning, PMLR 202. (ICML 2023). Available online

• Raj, V., Cui, T., Heinonen, M., and Marttinen, P. (2023). Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. In Proceedings of 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:6741-6763. (AISTATS 2023) Available online

• 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). Available online

• Cui, T., Havulinna, A., Marttinen, P.*, and Kaski, S.* (2022). Informative Bayesian Neural Network Priors for Weak Signals. Bayesian Analysis, 17(4):1121-1511. (*equal contribution) Available online

• 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

• 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

Selected Articles (Computational Biology and Health)

• Honkamaa, J., Khan, U., Koivukoski, S., Valkonen, M., Latonen, L., Ruusuvuori, P., and Marttinen, P. (2023). Deformation equivariant cross-modality image synthesis with paired non-aligned training data. Medical Image Analysis, 90:102940. Available online

• Ji, S. and Marttinen, P. (2023). Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2023). Available online

• 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., 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

• 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)

• Ojala, F.*, Abdul Sater, M.R.*, Miller, L.G., McKinnell, J.A., Hayden, M.K., Huang, S.S., Grad, Y.H.+, and Marttinen, P.+ (2023). Bayesian modeling of the impact of antibiotic resistance on the efficiency of MRSA decolonization. PLOS Computational Biology, accepted. (*/+ equal contribution) Preprint

• 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 (co-)authored the following software. For other code related to subsequent articles by the group, see the respective articles.

• 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

I am the responsible professor in the Major of Machine Learning, Data Science and Artificial Intelligence (Macadamia).

Courses I have taught:

• Special course: Seminar on NLP Research: 2023
• Machine Learning: 2023, 2022
• Special course: Elements of Causal Inference: 2022, 2021
• Machine Learning: Advanced Probabilistic Methods: 2023, 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