Seminars and Events

Artificial Intelligence Seminar

Machine Learning for Health

Event Details

May 21, 2026

Virtual Webinar Zoom Link

Webinar ID:  966 0168 7230

Passcode:  524889

Abstract:

Vast quantities of data combined with massive computing resources have made it achieve human-level performance on a wide range of AI tasks.  I will describe four projects done in collaboration with Cornell’s Jacobs Center for Precision Nutrition and Health and Cornell’s Architectural Robots Lab that bring machine learning to problems in healthcare: diagnosing TB x-rays, predicting end-line hemoglobin concentrations in human subjects using baseline physiological data, measuring blood pressure using photo-plethysmography, and building a robotic table and lamp for mobility limited individuals.  Time permitting I’ll also discuss my educational activities in making AI reachable to people without technical training.

Host: Yolanda Gil

POC: Alma Nava + Justina Gilleland

Speaker Bio

Haym Hirsh is a Professor in the Department of Computer Science at Cornell University. His research has focused on foundations and applications of machine learning, data mining, information retrieval, and artificial intelligence, especially targeting questions that integrally involve both people and computing. Most recently these interests have turned to crowdsourcing, human computation, and collective intelligence. Haym received his BS from the Mathematics and Computer Science Departments at UCLA and his MS and PhD from the Computer Science Department at Stanford University. Prior to moving to Cornell in 2013 to serve as Dean of the Faculty of Computing and Information Science Haym spent 24 years on the Computer Science faculty at Rutgers University, and has had visiting positions at AT&T Labs, Bar-Ilan University, Carnegie Mellon University, MIT, and the University of Zurich. From 2006-2010 he served as Director of the Division of Information and Intelligent Systems at the National Science Foundation. In 2022 he was elected a Fellow of the American Association for the Advancement of Science.