Suchi Saria is the John C. Malone Assistant Professor at Johns Hopkins University where she directs the Machine Learning and Healthcare Lab. Her work with the lab enables new classes of diagnostic and treatment planning tools for healthcare—tools that use statistical machine learning techniques to tease out subtle information from “messy” observational datasets, and provide reliable inferences for individualizing care decisions.
Saria’s methodological work spans Bayesian and probabilistic approaches for addressing challenges associated with inference and prediction in complex, real-world temporal systems, with a focus in reliable ML, methods for counterfactual reasoning, and Bayesian nonparametrics for tackling sample heterogeneity and time-series data.
Her work has received recognition in numerous forms including best paper awards at machine learning, informatics, and medical venues, a Rambus Fellowship (2004-2010), an NSF Computing Innovation Fellowship (2011), selection by IEEE Intelligent Systems to Artificial Intelligence’s “10 to Watch” (2015), the DARPA Young Faculty Award (2016), MIT Technology Review’s ‘35 Innovators under 35’ (2017), the Sloan Research Fellowship in CS (2018), the World Economic Forum Young Global Leader (2018), and the National Academies of Medicine (NAM) Emerging Leader in Health and Medicine (2018). In 2017, her work was among four research contributions presented by Dr. France Córdova, Director of the National Science Foundation to Congress’ Commerce, Justice Science Appropriations Committee. Saria received her PhD from Stanford University working with Prof. Daphne Koller.