Tuomo Hartonen – Principal investigator

I work at the intersection of machine learning, health and genomics. My approach is to apply machine learning to real-world data, acknowledging the real-world limitations and problems such as incomplete data, privacy issues and model fairness. Since 2025, I have worked as an Academy Research Fellow funded by the Research Council of Finland at the Finnish Institute for Molecular Medicine, FIMM, in Helsinki. My specific focus is developing multi-modal health AI models that can excel in public health-relevant tasks. Our team is currently developing a generative AI-based foundation model for public health, integrating nation-wide Finnish data on for example disease diagnoses, medication purchases and laboratory measurements, capable of forecasting future health trajectories.

Jonas Burian – PHD researcher (Joint with the DSGE lab)

Jonas holds a Master’s degree in Machine Learning from Aalto University, where he completed his thesis work at the DSGE lab. He further developed his expertise at the Fraunhofer Institute for Computer Graphics Research and the German Cancer Research Center, where he worked at the intersection of biomedical and machine learning techniques. His current research focuses on enhancing the transferability, scalability and privacy of multimodal AI for disease risk prediction. He is a PhD fellow at the Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard.

Hannah Terborg – Phd Researcher (joint with the DSGE lab)

Hannah holds a master’s degree in Life Science Technologies from Aalto University and a bachelor’s degree in Cognitive Science from the University of Osnabrück. Before joining the DSGE lab, she had applied various computational methods, including machine learning, to neuroimaging data. As part of her PhD,​​ she will be utilizing transformer-based models for disease risk prediction.

Chelsea Alvarado – PhD researcher (joint with the DSGE lab)

Chelsea is a doctoral researcher with a multidisciplinary background in psychology, biology, and data science. She holds a bachelor’s degree in Psychology from Sweet Briar College and a master’s in Data Science from the University of Virginia. She worked as a biomedical data scientist at the National Institute on Aging’s Center for Alzheimer’s and Related Dementias, where she contributed to omicSynth, published in the American Journal of Human Genetics in 2024, and at HHMI’s Janelia Research Campus on the fruit fly connectome. Her research interests lie at the intersection of neurogenetics, deep learning, and health equity.