Research


Our research focuses on computational analysis of complex natural and social systems. There is a great demand for targeted computational techniques to extract information and insights from rich data collections based on clever combinations of human and machine intelligence. We blend elements from fields such as machine learning/AI, probabilistic programming, statistical ecology, and data science, and drive open developer communities that help to translate latest theoretical advances into accessible methods to inform modeling, experimentation, and decision-making. For a full list of publications check this page.

Computational and data science: selected examples

Exit time as a measure of ecological resilience
Arani B, Nes E, Lahti L, Carpenter S & Scheffer M.
Science 372(6547), 2021
10.1126/science.aay4895

Wrangling with non-standard data
Mäkelä E, Lagus K, Lahti L, Säily T, Tolonen M, Hämäläinen M, Kaislaniemi S & Nevalainen T.
Proc. Digital humanities in the nordic countries 2612, 2020
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Retrieval and analysis of eurostat open data with the eurostat package
Lahti L, Huovari J, Kainu M & Biecek P.
The R Journal 9(1), 2017
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Three-species lotka-volterra model with respect to caputo and caputo-fabrizio fractional operators
Khalighi M, Eftekhari L, Hosseinpour S & Lahti L.
Symmetry 13, 2020
10.3390/sym13030368

More publications in computational and data science

All publications