University of Tartu (Tartu Ülikool)

CEIDSS is a non-profit Research Organization working in health promotion focused on the social determinants of health.  Its mission is to produce innovative intervention research to improve the health status of the population, with an emphasis education on health determinants and risk factors linked with non-communicable diseases.

CEIDSS works in association with local and national authorities. While participating in European projects, CEIDSS has a strong experience in developing national and international comprehensive programs on tackling malnutrition, aligned with health strategies and priorities.

The University of Tartu founded in 1632, is Estonia’s oldest and largest university. Serving as Estonia’s research and training hub, UTARTU excels in diverse fields, managing work-intensive projects and ranking among the top 1.2% globally. The Institute of Genomics, a merger of the Estonian Genome Centre and Estonian Biocentre, explores the impact of genetic, lifestyle, and environmental factors on health. Hosting the Estonian Biobank, it comprises 200,000 individuals with comprehensive health and genomic data, fostering groundbreaking research.


The team members

Jon Anders Eriksson

Jon Anders Eriksson, PhD, an ERA Chair holder and Associate Professor, specializes in interdisciplinary research in genomics. With a strong background in evolutionary genetics, he employs computational modeling and quantitative analysis at the nexus of medical genomics, anthropology, population genetics, and palaeoecology. Jon has a prolific publication record with 69 peer-reviewed papers and an h-index of 22. His current focus is on understanding how past climate, diets, and pathogens have influenced genetic diversity in modern populations, particularly in metabolic, cardiovascular, and immune-mediated diseases. Using an interdisciplinary approach, he combines mathematics, statistics, evolutionary biology, and medical genomics to identify novel genetic variants, reconstruct the evolutionary history of disease-associated variants, and improve disease risk prediction

Nika Mikailava

Nika is an expert in machine learning, passionate about using AI to address biological challenges. She has diverse experience in Virology, Plant Physiology, and Evolutionary Biology. She is ready to apply machine learning skills to obesity prediction and contribute to cutting-edge solutions.

J. Rodrigo Flores

Rodrigo is a PhD student working on the topics of ancient DNA imputation, natural selection inference and the evolution of complex traits in the human population. He has a long-term interest and formal education in evolutionary genetics, genomics, bioinformatics and data science.