University of Pittsburgh

Improving Causal Discovery with Data Integration

Assistant Professor
Friday, October 25, 2019 - 12:30pm - 1:30pm

Abstract: Causal modeling is important in biomedicine because it describes a system’s behavior not only under observation but also under intervention. Graphical causal models connect causal properties of a system to probabilistic properties under observation and intervention. My research integratively analyzes data sets collected under different experimental conditions, and possibly measuring different variables, to reverse-engineer causal models that are consistent all of the data. Integrating multiple datasets improves causal discovery and leads to novel inferences.

Bio: Sofia Triantafillou is an Assistant Professor in the Department of Biomedical Informatics in the University of Pittsburgh. After getting a PhD in the University of Crete, she was a postdoc in Northwestern University and the University of Pennsylvania. Her research focuses in designing causal discover algorithms and applying them to make novel inferences in biomedicine.

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