Jussi Määttä

[In English] [Suomeksi]


Postdoctoral Researcher
Department of Computer Science
University of Helsinki, Finland


E-mail (academic): jussi.maatta ‘at’ helsinki.fi
E-mail (personal/business): jussi ‘at’ maatta.name
Tel: +358–50–3401075
WWW: https://jussimaatta.com/
LinkedIn: https://www.linkedin.com/in/jmaatta
ORCID iD: 0000-0003-2613-1204


Ph.D. (2016) in Computer Science, University of Helsinki.
M.Sc. (2012) in Applied Mathematics, University of Helsinki.
B.Sc. (2011) in Mathematics, University of Helsinki.

Research interests

Employment history

Postdoctoral Researcher, Department of Computer Science, University of Helsinki.
Working in the Information, Complexity and Learning research group, primarily involved in the MachQu project funded by the Academy of Finland.

12/2016—8/2018: Senior Data Scientist, Swap.com.
4/2016—12/2016: Data Scientist, Solita Oy.
2/2016—3/2016: Software Engineer, Space Systems Finland Oy.
6/2013—2/2016: Doctoral Student, Department of Computer Science, University of Helsinki.


Refereed journal articles

  1. Määttä, J. & Roos, T. (2016). Maximum Parsimony and the Skewness Test: A Simulation Study of the Limits of Applicability. PLoS ONE, 11(4), e0152656. [doi]
  2. Määttä, J., Schmidt, D. F., & Roos, T. (2016). Subset Selection in Linear Regression using Sequentially Normalized Least Squares: Asymptotic Theory. Scandinavian Journal of Statistics, 43(2), 382–395. [doi] [postprint] [supplement]

Refereed conference and workshop articles

  1. Määttä, J. & Roos, T. (2016). Robust Sequential Prediction in Linear Regression with Student’s t-distribution. In Proc. 14th International Symposium on Artificial Intelligence and Mathematics (ISAIM 2016). [pdf1] [pdf2]
  2. Määttä, J., Siltanen, S., & Roos, T. (2014). A fixed-point image denoising algorithm with automatic window selection. In Proc. 5th European Workshop on Visual Information Processing (EUVIP 2014). [doi] [postprint]
  3. Austrin, P., Kaski, P., Koivisto, M., & Määttä, J. (2013). Space–time tradeoffs for subset sum: An improved worst case algorithm. In Proc. 40th International Colloquium on Automata, Languages and Programming (ICALP 2013). [doi] [arxiv]