Leo Lahti profile picture
Leo
Lahti
Professor, Data analytics
Professor

Contact

+358 29 450 2390
+358 50 436 4626
Vesilinnantie 5
20500
Turku

Links

Research homepage
CV_Lahti_TENK.pdf
ORCID

Areas of expertise

Data science
AI
Machine Learning
Applied statistics
Statistical programming
Probabilistic models
Complex natural and social systems
Microbial ecology
Computational humanities
Open knowledge

Biography

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

Teaching

Computational and data science, statistical and probabilistic programming, machine learning, AI, applied statistics, ecological models, open science

Research

Computational scientist focusing on change in complex natural and social systems, and how they can be understood through a computational lens.

Publications

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Probabilistic early warning signals (2021)

Ecology and Evolution
Laitinen Ville, Dakos Vasilis, Lahti Leo
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1))

Taxonomic signatures of cause-specific mortality risk in human gut microbiome (2021)

Nature Communications
Salosensaari Aaro, Laitinen Ville, Havulinna Aki S., Meric Guillaume, Cheng Susan, Perola Markus, Valsta Liisa, Alfthan Georg, Inouye Michael, Watrous Jeramie D., Long Tao, Salido Rodolfo A., Sanders Karenina, Brennan Caitriona, Humphrey Gregory C., Sanders Jon G., Jain Mohit, Jousilahti Pekka, Salomaa Veikko, Knight Rob, Lahti Leo, Niiranen Teemu
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1))

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment (2021)

Frontiers in Microbiology
Marcos-Zambrano Laura Judith, Karaduzovic-Hadziabdic Kanita, Loncar-Turukalo Tatjana, Przymus Piotr, Trajkovik Vladimir, Aasmets Oliver, Berland Magali, Gruca Aleksandra, Hasic Jasminka, Hron Karel, Klammsteiner Thomas, Kolev Mikhail, Lahti Leo, Lopes Marta B., Moreno Victor, Naskinova Irina, Org Elin, Paciência Inês, Papoutsoglou Georgios, Shigdel Rajesh, Stres Blaz, Vilne Baiba, Yousef Malik, Zdravevski Eftim, Tsamardinos Ioannis, Carrillo de Santa Pau Enrique, Claesson Marcus J., Moreno-Indias Isabel, Truu Jaak; on behalf of ML4Microbiome
(Vertaisarvioitu katsausartikkeli tieteellisessä aikakauslehdessä (A2))

Efficient computation of Faith's phylogenetic diversity with applications in characterizing microbiomes (2021)

Genome Research
Armstrong George, Cantrell Kalen, Huang Shi, McDonald Daniel, Haiminen Niina, Carrieri Anna Paola, Zhu Qiyun, Gonzalez Antonio, McGrath Imran, Beck Kristen L, Hakim Daniel, Havulinna Aki S, Meric Guillaume, Niiranen Teemu, Lahti Leo, Salomaa Veikko, Jain Mohit, Inouye Michael, Swafford Austin D, Kim Ho-Cheol, Parida Laxmi, Vázquez-Baeza Yoshiki, Knight Rob
(Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1))
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