Leo Lahti
Professor
leo.lahti@utu.fi +358 29 450 2390 +358 50 436 4626 Vesilinnantie 5 Turku Työhuone: 452E ORCID-tunniste: https://orcid.org/0000-0001-5537-637X |
Data science; AI; Machine Learning; Applied statistics; Statistical programming; Probabilistic models; Complex natural and social systems; Microbial ecology; Computational humanities; Open knowledge
Leo Lahti is professor in Data Science in University of Turku, Finland. His research team focuses on computational analysis and modeling of complex natural and social systems. Lahti obtained doctoral degree (DSc) from Aalto University in Finland (2010), developing probabilistic machine learning methods for high-throughput life science data integration. This was followed by subsequent postdoctoral research at EBI/Hinxton (UK), Wageningen University (NL), and VIB/KU Leuven (BE). Lahti has coordinated international networks in data science methods and applications and organizes international data science training events on a regular basis. He is vice chair for the national coordination on open science Finland, executive committee member for the International Science Council Committee on Data (2023-2025), member of the global Bioconductor Community Advisory Board, and founder of the open science work group of Open Knowledge Finland ry. For more information, see the research homepage iki.fi/Leo.Lahti
Computational scientist focusing on change in complex natural and social systems, and how they can be understood through a computational lens.
Computational and data science, statistical and probabilistic programming, machine learning, AI, applied statistics, ecological models, open science
- Prebiotic Xylo-Oligosaccharides Ameliorate High-Fat-Diet-Induced Hepatic Steatosis in Rats (2020)
- Nutrients
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Quantifying bias and uncertainty in historical data collections with probabilistic programming (2020)
- CEUR Workshop Proceedings
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Unsupervised hierarchical clustering identifies a metabolically challenged subgroup of hypertensive individuals (2020)
- Journal of Clinical Hypertension
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Wrangling with non-standard data (2020)
- CEUR Workshop Proceedings
(A4 Vertaisarvioitu artikkeli konferenssijulkaisussa) - Analytical determination of editions from bibliographic metadata (2019)
- Studia Humaniora Ouluensia
(A3 Vertaisarvioitu kirjan tai muun kokoomateoksen osa) - A National Public Sphere? Analyzing the Language, Location, and Form of Newspapers in Finland, 1771–1917 (2019)
- Journal of European Periodical Studies
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - A Quantitative Approach to Book-Printing in Sweden and Finland, 1640–1828 (2019)
- Historical Methods: A Journal of Quantitative and Interdisciplinary History
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Best practices in bibliographic data science (2019)
- Studia Humaniora Ouluensia
(A3 Vertaisarvioitu kirjan tai muun kokoomateoksen osa) - Bibliographic Data Science and the History of the Book (c. 1500–1800) (2019)
- Cataloging and Classification Quarterly
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä ) - Gut microbiota composition is associated with temperament traits in infants (2019)
- Brain, Behavior, and Immunity
(A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä )



