Prof. Roch Guérin

Abstract:
Edge computing solutions have proliferated, fueled by a combination of increased network ubiquity, advances in computing, especially in embedded devices, and by the growing need to bring computations closer to where data is produced. Many of those scenarios are driven by machine learning applications. In this talk, I will discuss two projects, outlined below, both motivated by edge computing machine learning applications, and for which machine learning was itself instrumental in devising an efficient solution. I will conclude the talk with a brief reflection on my experience with machine learning as a (powerful) tool for solving complex optimization problems.

1. The first project [1] targeted object detection with local and edge compute resources cooperating to optimize detection accuracy under load constraint on the edge server. Under such a constraint, the goal is to devise a simple policy to decide which images to offload to the edge server while maximizing detection accuracy. This calls for a metric that quantifies improvements in overall detection accuracy from offloading an individual image, and an estimator for that metric that can run on embedded devices. The benefits of the approach are evaluated experimentally.

2. The second project dealt with an object classification problem where a camera is uploading images to an edge server for classification [2]. The wireless network used to upload successive images is, however, subject to bandwidth fluctuations. This requires an adaptive transmission strategy to maximize inference accuracy, irrespective of the amount of data that can be transmitted for each image. We realize this through a simple application of stochastic tail-drop when training a neural compression algorithm and demonstrate the efficacy of the approach on a local testbed.

[1] J. Qiu, R. Wang, B. Hu, R. Guerin, and C.Lu, "Optimizing Edge Offloading Decisions for Object Detection." 2024 ACM/IEEE Symposium on Edge Computing (SEC 2024), Rome, Italy, December 2024.
[2] R. Wang, H. Liu, J. Qiu, M. Xu, R. Guerin, and C.Lu, "Progressive Neural Compression for Adaptive Image Offloading Under Timing Constraints." Best Student Paper Award, 2023 IEEE Real-Time Systems Symposium (RTSS), December 2023, Taipei, Taiwan.

Biography:

Biobraphy:

Roch Guerin is the Harold B. and Adelaide G. Welge Professor and Chair of Computer Science and Engineering at Washington University in Saint Louis, which he joined in 2013. He previously was the Alfred Fitler Moore Professor of Telecommunications Networks in the Electrical and System Engineering department of the University of Pennsylvania, which he joined in October 1998. Prior to joining Penn, he spent 12 years at the IBM T. J. Watson Research Center in a variety of technical and management positions. He was on leave from Penn between 2001 and 2004, starting Ipsum Networks, a company that pioneered the concept of route analytics for managing IP networks. Roch received his Ph.D. from Caltech and did his undergraduate at ENST in France. He is an ACM and IEEE Fellow. He served as the Editor-in-Chief for the IEEE/ACM Transactions on Networking and as the Chair of ACM SIGCOMM. In 1994 he received an IBM Outstanding Innovation Award for his work on traffic management. He received the IEEE TCCC Outstanding Service Award in 2009 and was the recipient of the 2010 INFOCOM Achievement Award for "Pioneering Contributions to the Theory and Practice of QoS in Networks"

Address:Department of Computer Science and Engineering Washington University in St. Louis, , United States