Suchi Saria

Saria is from Darjeeling.^[7] She earned her bachelor's degree at Mount Holyoke College.^[8] She was awarded a full scholarship from Microsoft. In 2004 she joined Stanford University as a Rambus Corporation Fellow.^[8] She earned her Master of Science and Doctor of Philosophy^[9] degrees at Stanford University, supervised by Daphne Koller and advised by Anna Asher Penn and Sebastian Thrun. At Stanford University, Saria developed a statistical model that could predict premature baby outcomes with a 90% accuracy.^[10] The model used data from monitors, birth weight and length of time spent in the womb to predict whether a preemie would develop an illness.^[11][12] She worked in the startup Aster Data Systems.^[13]

Saria believes that big data can be used to personalise healthcare.^[14][15] She is considered an expert in computational statistics and their applications to the real world.^[8] She uses Bayesian and probabilistic modelling.^[7] In 2014 Saria was funded by a $1.5 million Gordon and Betty Moore Foundation project that looked to make intensive care units safer.^[16] The project used data collected at patients' bedsides along with noninvasive 3D sensors that monitor care in patient's hospital rooms.^[17] The sensors collect information on steps that might have been missed by doctors; like washing hands.^[17]

Saria uses big data to manage chronic diseases.^[18] She is part of a National Science Foundation (NSF) award that looks at scleroderma. She uses machine learning to analyse medical records and identify similar patterns of disease progression.^[18] The system works out which treatments have been effectively used for various symptoms to aid doctors in choosing treatment plans for specific patients.^[18] She has developed another algorithm that can be used to predict and treat Septic shock.^[19] The algorithm used 16,000 items of patient health records and generates a targeted real-time warning (TREWS) score.^[20] She collaborated with physicians to use the algorithm in clinics, and it was correct 86% of the time. Saria modified the algorithm to avoid missing high risk patients- for example, those who have suffered from septic shock previously and who have sought successful treatment.^[21] She was described by XRDS magazine as being a Pioneer in transforming healthcare.^[22] In 2016 Saria spoke at about using machine learning for medicine at TEDxBoston.^[23] The talk has been viewed over 100,170 times.^[24]

Her awards and honors include:

• 2018 Sloan Research Fellowship^[25][26][27]
• 2018 World Economic Forum Young Global Leader^[27]
• 2017 MIT Technology Review 35 Innovators Under 35^[1]
• 2017 Defense Advanced Projects Research Agency (DARPA) Young Faculty Fellowship^[28]
• 2016 Brilliant 10 award by Popular Science^[29]
• 2015 IEEE Intelligent Systems Young Star in Artificial Intelligence^[30]
• 2015 Johns Hopkins Discovery Award^[8]
• 2014 National Science Foundation (NSF) Smart and Connected Health Research Grant^[14]
• 2014 Google Research Award^[8]
• 2014 Society of Critical Care Medicine Annual Scientific Award^[8]
• 2013 Gordon and Betty Moore Foundation Research Award^[8]