About the series
We invite you to our exciting upcoming CISE Distinguished Lecture featuring Rajeev Alur, the esteemed Zisman Family Professor of Computer and Information Science and Founding Director of the ASSET Center for Trustworthy AI at the University of Pennsylvania. Don’t miss it!
Bio:
Rajeev Alur is Zisman Family Professor of Computer and Information Science and the Founding Director of ASSET Center for Trustworthy AI at University of Pennsylvania. He obtained his bachelor's degree from IIT Kanpur and PhD from Stanford University. Before joining Penn, he was with Computing Science Research Center at Bell Labs. His research is focused on principles and tools for design and analysis of safe and trustworthy systems. Notable awards include the inaugural CAV (Computer-Aided Verification) award, the inaugural Alonzo Church award, IIT Kanpur Distinguished Alumnus Award, and the Knuth Prize. He is the author of the textbook Principles of Cyber-Physical Systems (MIT Press), has served as the chair of ACM SIGBED (Special Interest Group on Embedded Systems), was the lead PI of the NSF Expeditions in Computing project ExCAPE on program synthesis, and is the General Chair for the upcoming Federated Logic Conference (FLoC).
Abstract:
Recent advances in deep learning have led to novel AI-based solutions to challenging computational problems. Yet, the state-of-the-art models do not provide reliable explanations of how they make decisions, and can make occasional mistakes on even simple problems. The resulting lack of trust and reliability are obstacles to their adoption in safety-critical applications. Neurosymbolic learning architectures aim to address this challenge by bridging the complementary worlds of deep learning and logical reasoning via explicit symbolic representations. In this talk, I will describe representative neurosymbolic systems, and how they enable more accurate, interpretable, and domain-aware solutions to problems in healthcare and robotics.
Zoom Information
Topic: Neurosymbolic Systems for Trustworthy AI
Register in advance for this webinar:
https://nsf.zoomgov.com/webinar/register/WN_wQN47K3fT0ab4DchYhhL6Q
After registering, you will receive a confirmation email containing information about joining the webinar.
