Speaker: David Sontag, CIMS
Location: Warren Weaver Hall 1302
Date: November 4, 2011, 11:30 a.m.
Host: Dennis Shasha
Physicians in the emergency department must rapidly gather and synthesize large amounts of data from disparate sources in order to make treatment decisions, all while constantly switching between patients. Subtle changes in clinical parameters over time may be easily overlooked by even the most vigilant clinician.
This talk proposes the design of algorithms for real-time surveillance of patients’ medical records so that we can catch dangerous conditions before they become severe, provide context-specific displays of patient information to clinicians, and improve computerized decision support.
The challenge is to make accurate inferences from the diverse set of signals available for each patient, putting these signals in the context of the patient’s existing medical history. To do so, we must make progress toward solving a number of open problems in artificial intelligence.
David is an Assistant Professor in NYU's Computer Science department. His research focuses on theoretical and practical aspects of machine learning and probabilistic inference, with recent applications to medicine, natural language processing, and information retrieval. His group has a close collaboration with the Emergency Medicine Informatics Research Lab at Beth Israel Deaconess Medical Center.
Refreshments will be offered starting 15 minutes prior to the scheduled start of the talk.