Suchi Saria

Suchi Saria is the John C. Malone Associate Professor of Computer Science at the Whiting School of Engineering, with joint appointments in the Departments of Biostatistics and Health Policy and Management in the Bloomberg School of Public Health and in health system informatics at the School of Medicine. She directs the university’s Machine Learning and Healthcare Lab and is the founding research director of the Malone Center for Engineering in Healthcare.

Saria’s goal is to use sophisticated computer science and the deluge of data available in health care and other settings to individualize patient care and save lives. Her pioneering work centers on enabling new classes of diagnostic and treatment planning tools for health care—tools that use novel state-of-the-art AI techniques to tease out subtle information from “messy” observational datasets and provide reliable inferences for individualizing care decisions.

Her work provides an entry point into a future in which the data collected from a large number of patients may reliably inform physicians about the best treatment plans for individual patients. For instance, algorithms that she created are being used today in hospitals to predict, with startling accuracy, which patients will succumb to deadly sepsis, a condition that annually kills more people than breast and prostate cancer combined. This work led to her being named one of Popular Science’s Brilliant 10 in 2016, one of MIT Technology Review’s 35 Innovators Under 35 in 2017, and a member of the Forum of Young Global Leaders in 2018.

For another project, Saria and her team created an app that allows patients with Parkinson’s disease to track their symptoms on their personal smartphones. Rather than relying on the subjective observations of a medical staff member in a clinical setting, giving patients the ability to report on their symptoms at any time of day—whether they’re in a clinic or inside their own home—can better capture the day-to-day variability of Parkinson’s symptoms and provide doctors with a clearer picture of their patients’ overall health and how well their medications are working. This work is considered groundbreaking because though other mobile studies also collect data “in the wild,” few have found ways to validate it clinically, as that process requires the expensive and laborious collection of benchmark (also called “gold standard”) data at home. Saria solved this issue by developing a machine learning framework that uses “weak supervision”—information that is inexpensive and readily collected at home—to train algorithms for progression monitoring from mobile data.

Saria’s work has received wide recognition, including: best paper awards at machine learning, informatics, and medical venues; a Rambus Fellowship from 2004 to 2010; an NSF Computing Innovation Fellowship in 2011; the IEEE Intelligent Systems’ “AI’s 10 to Watch” award in 2015; a DARPA Young Faculty Award in 2016; a Sloan Research Fellowship in 2018; and a place on TIME’s Best Inventions of 2023.

In 2017, Saria’s work was among four research contributions presented by NSF Director France Córdova to the U.S. House of Representatives’ Commerce, Justice, Science, and Related Agencies Appropriations Committee. She was invited to join the National Academy of Engineering’s Frontiers of Engineering Symposium in 2017 and, in 2018, to join the National Academy of Medicine’s Emerging Leaders in Health and Medicine Program.

Saria received her PhD from Stanford University working with Daphne Koller. She joined the Johns Hopkins University in 2012.