Leo Lahti
Professor
leo.lahti@utu.fi +358 29 450 2390 +358 50 436 4626 Vesilinnantie 5 Turku Agora Office: 452E ORCID identifier: 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
- Association of Long-Term Habitual Dietary Fiber Intake since Infancy with Gut Microbiota Composition in Young Adulthood (2024)
- Journal of Nutrition
(Refereed journal article or data article (A1)) - Daily skin-to-skin contact alters microbiota development in healthy full-term infants (2024)
- Gut Microbes
(Refereed journal article or data article (A1)) - Fewer culturable Lactobacillaceae species identified in faecal samples of pigs performing manipulative behaviour (2024)
- Scientific Reports
(Refereed journal article or data article (A1)) - Integration of polygenic and gut metagenomic risk prediction for common diseases (2024)
- Nature Aging
(Refereed journal article or data article (A1)) - Role of Gut Microbiota in Statin-Associated New-Onset Diabetes—a Cross-Sectional and Prospective Analysis of the FINRISK 2002 Cohort (2024)
- Arteriosclerosis, Thrombosis, and Vascular Biology
(Refereed journal article or data article (A1)) - Shotgun metagenomic analysis of the oral microbiome in gingivitis: a nested case-control study (2024)
- Journal of Oral Microbiology
(Refereed journal article or data article (A1)) - Aberrations in the early pregnancy serum metabolic profile in women with prediabetes at two years postpartum (2023)
- Metabolomics
(Refereed journal article or data article (A1)) - Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action (2023)
- Frontiers in Microbiology
(Refereed journal article or data article (A1)) - A toolbox of machine learning software to support microbiome analysis (2023)
- Frontiers in Microbiology
(Refereed review article in scientific journal (A2)) - Author Correction: Greengenes2 unifies microbial data in a single reference tree (Nature Biotechnology, (2023), 10.1038/s41587-023-01845-1) (2023)
- Nature Biotechnology
(Other (O2)) - Bone marrow metabolism is affected by body weight and response to exercise training varies according to anatomical location (2023)
- Diabetes, Obesity and Metabolism
(Refereed journal article or data article (A1)) - Dealing with dimensionality: the application of machine learning to multi-omics data (2023)
- Bioinformatics
(Refereed review article in scientific journal (A2)) - Ebola epidemic model with dynamic population and memory (2023)
- Chaos, Solitons and Fractals
(Refereed journal article or data article (A1)) - Greengenes2 unifies microbial data in a single reference tree (2023)
- Nature Biotechnology
(Refereed journal article or data article (A1)) - Gut microbiome and atrial fibrillation: results from a large population-based study (2023)
- EBioMedicine
(Refereed journal article or data article (A1)) - Gut microbiota composition and function in pregnancy as determinants of prediabetes at two-year postpartum (2023)
- Acta Diabetologica
(Refereed journal article or data article (A1)) - Impacts of maternal microbiota and microbial metabolites on fetal intestine, brain, and placenta (2023)
- BMC Biology
(Refereed journal article or data article (A1)) - Infant gut microbiota and negative and fear reactivity (2023)
- Development and Psychopathology
(Refereed journal article or data article (A1)) - Machine learning approaches in microbiome research: challenges and best practices (2023)
- Frontiers in Microbiology
(Refereed review article in scientific journal (A2)) - Maternal microbiota communicates with the fetus through microbiota-derived extracellular vesicles (2023)
- Microbiome
(Refereed journal article or data article (A1))