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

Quantifying the impact of ecological memory on the dynamics of interacting communities
Khalighi M, Sommeria-Klein G, Gonze D, Faust K & Lahti L.
PLoS Computational Biology 18(6), 2022
10.1371/journal.pcbi.1009396

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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More publications in computational and data science

All publications

iSEEtree: Interactive explorer for hierarchical data
Benedetti G, Seraidarian E, Pralas T, Jeba A, Borman T & Lahti L.
arXiv 2024
10.48550/arXiv.2412.02882

Algorithm 1047: FdeSolver, a Julia package for solving fractional differential equations
Khalighi M, Benedetti G & Lahti L.
ACM Transactions on Mathematical Software 50(3), 2024
10.1145/3680280

Epidemic transmission modeling with fractional derivatives and environmental pathogens
Khalighi M, Lahti L & Ndaïrou F.
International Journal of Biomathematics 2024
10.48550/arXiv.2305.16689 | URL

Ebola epidemic model with dynamic population and memory
Ndaïrou F, Khalighi M & Lahti L.
Chaos, Solitons and Fractals 170, 2023
10.1016/j.chaos.2023.113361

FdeSolver: A Julia Package for Solving Fractional Differential Equations
Khalighi M, Benedetti G & Lahti L.
arXiv arXiv, 2022
10.48550/ARXIV.2212.12550 | URL

Probabilistic multivariate early warning signals
Laitinen V & Lahti L.
Springer Cham., 2022
10.1007/978-3-031-15034-0 | URL

Probabilistic early warning signals
Laitinen V, Dakos V & Lahti L.
Ecology & Evolution 11(20), 2021
10.1002/ece3.8123

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

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

Stability estimation of autoregulated genes under Michaelis-Menten type kinetics
Arani B, Mahmoudi M, Lahti L, González J & Wit E.
Physical Review E 97(6) American Physical Society, 2018
10.1103/PhysRevE.97.062407 | PDF

A hierarchical ornstein-uhlenbeck model for stochastic time series analysis
Laitinen V & Lahti L.
Advances in intelligent data analysis XVII. Lecture notes in computer science 11191. Springer, 2018
Conference proceedings.
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rOpenGov: Open source ecosystem for computational social sciences and digital humanities
Leo Lahti J & Kainu M.
software, 2013
ICML/MLOSS workshop (Int’l Conf. on Machine Learning - Open Source Software workshop).
URL

Dependency modeling toolkit
Lahti L, Tripathi A & Huovilainen O.
software; ICML workshop, 2010
CRAN: dmt
URL

NetResponse (functional network analysis)
Lahti L, Gusmao A & Huovilainen O.
software, 2010
R/Bioconductor: netresponse
10.18129/B9.BIOC.NETRESPONSE | URL

Pairwise integration of functional genomics data
Huovilainen O & Lahti L.
software, 2010
R/BioConductor: pint
URL

Dependency detection with similarity constraints
Lahti L, Myllykangas S, Knuutila S & Kaski S.
Proc. MLSP’09 IEEE international workshop on machine learning for signal processing XIX software, 2009
R/Bioconductor: pint
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Associative clustering for exploring dependencies between functional genomics data sets
Kaski S, Nikkilä J, Sinkkonen J, Lahti L, Knuuttila J & Roos C.
IEEE/ACM Transactions on Computational Biology and Bioinformatics 2(3), 2005
Special Issue on Machine Learning for Bioinformatics – Part 2
10.1109/TCBB.2005.32 | PDF

Associative clustering (AC): Technical details
Sinkkonen J, Kaski S, Nikkilä J & Lahti L.
Helsinki University of Technology, 2005
Publications in Computer and Information Science
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Associative clustering
Sinkkonen J, Nikkilä J, Lahti L & Kaski S.
Proceedings of the ECML’04, 15th european conference on machine learning Springer, 2004
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