WASP Lighthouse Seminar. Tarek Abdelzaher - The Bottlenecks in Advancing Foundation Models for Internet of Things (IoT) Applications

Please sign up at: https://survey.mailing.lu.se/Survey/62026 before Thursday Dec. 11th

This is a WASP Lighthouse kick-off event

Title: The Bottlenecks in Advancing Foundation Models for Internet of Things (IoT) Applications

Abstract: Advances in self-supervised AI revolutionized modern machine intelligence, but important challenges remain when applying these solutions in IoT contexts - specifically, on lower-end distributed embedded devices with multimodal specialized sensors, where ample training data are not readily available. The talk discusses the bottlenecks in advancing self-supervised machine intelligence services and foundation models for Internet of Things (IoT) applications. We argue that, at training time, the key bottlenecks are data-related. Embedded computing relies on scarce sensor data modalities, unlike those commonly addressed in mainstream AI, necessitating solutions for efficient learning from scarce sensor data. At inference time, the bottlenecks are resource-related, calling for improved resource economy and novel scheduling policies. Further ahead, the convergence of AI around large language models (LLMs) introduces additional model-related challenges in embedded contexts. The paper discusses novel research directions in addressing these bottlenecks, covering data-, resource-, and model-related challenges in the embedded sensing domain.

Bio: Tarek Abdelzaher received his Ph.D. in Computer Science from the University of Michigan in 1999. He is currently a Sohaib and Sara Abbasi Professor and Willett Faculty Scholar at the Department of Computer Science, the University of Illinois at Urbana Champaign. He has authored/coauthored more than 450 refereed publications in edge AI, IoT, real-time computing, sensor networks, and control. He served as an Editor-in-Chief of the Journal of Real-Time Systems, and has served as Associate Editor of the IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE Embedded Systems Letters, the ACM Transaction on Sensor Networks, and the Ad Hoc Networks Journal, among others. Abdelzaher's research interests lie broadly in understanding and influencing performance and temporal properties of networked embedded, social, and software systems in the face of increasing complexity, distribution, and degree of interaction with an external physical environment. Tarek Abdelzaher is a recipient of the IEEE Outstanding Technical Achievement and Leadership Award in Real-time Systems (2012), the Xerox Award for Faculty Research (2011), as well as over a dozen best paper awards. He is a fellow of IEEE and ACM.