Overview

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Speakers 

Suchi Saria, John C. Malone Associate Professor of Computer Science at the Whiting School of Engineering, joint appointments with Departments of Biostatistics and Health Policy and Management, Johns Hopkins University

James Zou, Associate Professor of Biomedical Data Science and, by courtesy, of CS and EE at Stanford

Moderator 

Nhat Ho, Associate Professor of Statistics and Data Sciences at the University of Texas at Austin

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About the Speakers 

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 healthcare 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 healthcare—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. 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. She was named in the Time100 in AI list and a CNBC Changemaker in 2026. 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. See Profile

James Zou is an Associate Professor of Biomedical Data Science and, by courtesy, of CS and EE at Stanford. He leads the Stanford AI for Science Lab, with a particular interest in tackling challenges in human disease and health. Their algorithms have received FDA approval, are used by millions of developers, and were selected as The New York Times' Good Tech. James Zou received his Ph.D. from Harvard in 2014 and has held positions at Microsoft Research, as a Gates Scholar at Cambridge, and as a Simons Fellow at UC Berkeley. He joined Stanford in 2016, where he is a two-time Chan–Zuckerberg Investigator, a faculty director at the Stanford Data Science Institute, and a member of the Stanford AI Lab. His research has been recognized with the Overton Prize, a Sloan Fellowship, an NSF CAREER Award, multiple best paper awards, and faculty awards from Google, Amazon, Genentech, and Apple. See Profile

About the Moderator

About the COPSS-NISS Leadership Webinar Series 

COPSS (Committee of the Presidents of Statistical Societies) and NISS have come together to organize and host a new webinar series focusing on leadership in statistics and data science. Plan to attend these webinars every month during the academic year! Visit the COPSS-NISS Leadership Series Page for previous webinars.

The COPSS-NISS Leadership Webinar Series is co-organized by the Committee of the Presidents of Statistical Societies (COPSS) Emerging Leaders in Statistics and the National Institute of Statistical Sciences (NISS). The purpose of the webinar series is to promote leadership skills for members of the statistical societies at any stage in their careers. The series features conversations with leaders throughout the discipline, including leaders from major academic and government institutions, and companies. Invited speakers share their leadership stories and answer questions about their experiences. Each webinar is moderated by a member of the COPSS Emerging Leaders in Statistics program.

Access the Full COPSS-NISS Leadership Webinar Series YouTube Playlist | COPSS-NISS Leadership Webinar Series: https://www.youtube.com/playlist?list=PLoRtupvDJTjvFukMcO6NfDr0GvxDsIj81

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Save the Dates! 

• Tuesday, February 23, 2027
• Tuesday, March 30, 2027
• Tuesday, April 27, 2027
• Tuesday, May 25, 2027