Marinka research investigates machine learning for biomedical sciences, focusing on large networks of interactions between biomedical entities--e.g., proteins, drugs, diseases, and patients. She leverages these networks at the scale of billions of interactions among millions of entities and develop new methods blending machine learning with statistical methods and network science.
She uses these methods to answer burning scientific questions, such as how Darwinian evolution changes molecular networks, and how data-driven algorithms accelerate scientific discovery; and to solve high-impact problems, such as what drugs and combinations of drugs are safe for patients, what molecules will treat what diseases, and how newborns are transferred between hospitals and how these transfers influence outcomes.
Marinka received a Ph.D. in Computer Science from University of Ljubljana in 2015 while also researching at Imperial College London, University of Toronto, Baylor College of Medicine, and Stanford University. She obtained a B.Sc. in Computer Science and Mathematics in 2012.
Starting in December 2019, she will be a tenure-track Assistant Professor at Harvard University, and her laboratory at Harvard Medical School will focus on Machine Learning for Science and Medicine.