Leo Lahti
Professor
leo.lahti@utu.fi +358 29 450 2390 +358 50 436 4626 Vesilinnantie 5 Turku : 452E |
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
- Modelling spatial patterns in host-associated microbial communities (2021)
- Environmental Microbiology
- Probabilistic analysis of early modern British book prices (2021)
- CEUR Workshop Proceedings
- Probabilistic early warning signals (2021)
- Ecology and Evolution
- Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions (2021)
- Frontiers in Microbiology
- Systemic cross-talk between brain, gut, and peripheral tissues in glucose homeostasis: effects of exercise training (CROSSYS). Exercise training intervention in monozygotic twins discordant for body weight (2021)
- BMC sports science, medicine and rehabilitation
- Targeting Gut Microbiota to Treat Hypertension: A Systematic Review (2021)
- International Journal of Environmental Research and Public Health
- Taxonomic signatures of cause-specific mortality risk in human gut microbiome (2021)
- Nature Communications
- Three-Species Lotka-Volterra Model with Respect to Caputo and Caputo-Fabrizio Fractional Operators (2021)
- Symmetry
- Xylo-Oligosaccharides in Prevention of Hepatic Steatosis and Adipose Tissue Inflammation: Associating Taxonomic and Metabolomic Patterns in Fecal Microbiomes with Biclustering (2021)
- International Journal of Environmental Research and Public Health
- Association Between the Gut Microbiota and Blood Pressure in a Population Cohort of 6953 Individuals (2020)
- Journal of the American Heart Association
- Diet, perceived intestinal well-being and compositions of fecal microbiota and short chain fatty acids in oat-using subjects with celiac disease or gluten sensitivity (2020)
- Nutrients
- Eicosanoid Inflammatory Mediators Are Robustly Associated With Blood Pressure in the General Population (2020)
- Journal of the American Heart Association
- Gut Microbiota and Host Gene Mutations in Colorectal Cancer Patients and Controls of Iranian and Finnish Origin (2020)
- Anticancer Research
- Intestinal microbiota analysis supports inclusion of gluten-free oats to diet of subjects with celiac disease or gluten sensitivity (2020)
- Proceedings of the Nutrition Society
- Maternal prenatal psychological distress and hair cortisol levels associate with infant fecal microbiota composition at 2.5 months of age (2020)
- Psychoneuroendocrinology
- Partial restoration of normal intestinal microbiota in morbidly obese women six months after bariatric surgery (2020)
- PeerJ
- Prebiotic Xylo-Oligosaccharides Ameliorate High-Fat-Diet-Induced Hepatic Steatosis in Rats (2020)
- Nutrients
- Quantifying bias and uncertainty in historical data collections with probabilistic programming (2020)
- CEUR Workshop Proceedings
- Unsupervised hierarchical clustering identifies a metabolically challenged subgroup of hypertensive individuals (2020)
- Journal of Clinical Hypertension
- Wrangling with non-standard data (2020)
- CEUR Workshop Proceedings