ACM ByteCast Episode 15: Suchi Saria.
In this episode of ACM ByteCast, Rashmi Mohan hosts Suchi Saria, the John C. Malone Associate Professor of Machine Learning and Healthcare at Johns Hopkins University, where she uses big data to improve patient outcomes. She directs the Machine Learning and Healthcare Lab and is the founding research director of the Malone Center for Engineering in Healthcare. Saria has worked on projects with the NSF, NIH, DARPA, and the FDA and is the founder of Bayesian Health. Her many recognitions include Popular Science magazine’s “Brilliant 10”, the MIT Technology Review’s 35 Innovators Under 35, and World Economic Forum Young Global Leader.
Suchi describes tinkering with LEGO Mindstorm and reading about AI and the future as a child in India and how, years later, she ended up at the forefront of applying machine learning techniques to computational biology. She explains how ML can help healthcare go from a reactive to a predictive and preventive model, and the challenge of making sure that the medical data collected is actionable, interpretable, safe, and free of bias. She also talks about the transition from research to practice and offers her best advice for students interested in pursuing computing.
https://learning.acm.org/bytecast/ep15-suchia-saria
In this episode of ACM ByteCast, Rashmi Mohan hosts Suchi Saria, the John C. Malone Associate Professor of Machine Learning and Healthcare at Johns Hopkins University, where she uses big data to improve patient outcomes. She directs the Machine Learning and Healthcare Lab and is the founding research director of the Malone Center for Engineering in Healthcare. Saria has worked on projects with the NSF, NIH, DARPA, and the FDA and is the founder of Bayesian Health. Her many recognitions include Popular Science magazine’s “Brilliant 10”, the MIT Technology Review’s 35 Innovators Under 35, and World Economic Forum Young Global Leader.
Suchi describes tinkering with LEGO Mindstorm and reading about AI and the future as a child in India and how, years later, she ended up at the forefront of applying machine learning techniques to computational biology. She explains how ML can help healthcare go from a reactive to a predictive and preventive model, and the challenge of making sure that the medical data collected is actionable, interpretable, safe, and free of bias. She also talks about the transition from research to practice and offers her best advice for students interested in pursuing computing.
https://learning.acm.org/bytecast/ep15-suchia-saria
