Research Talk by Prof. Romaric Duvignau: "Data-Driven and Efficient Algorithms for Enhanced Communication at the Edge"

When: Wednesday, June 25th 10:00-11:00
Where: At COMSYS’ Seminar Room – Computer Science Department, Building E3, Room 9007
Title: Data-Driven and Efficient Algorithms for Enhanced Communication at the Edge

Abstract Communication is the cornerstone element of many distributed, cyber-physical, or IoT systems (e.g., vehicular networks, smart grid, sensor networks). Transmitting data in the most efficient manner is often a requirement for many applications to reduce costs and boost performance. What is needed in this context are dedicated (i.e., data- or use-case-driven) and efficient algorithms that must be designed, analyzed, and experimentally validated, taking into account both the communication network and the distribution and continuous aspect of the generated data. My research centers on the design, study, and evaluation of those distributed and application-driven algorithms to perform optimization on a continuous basis and decrease computational burden and/or communication costs. In this short presentation, I will focus on recent advancements we have for two specific applications: (1) sharing energy resources among households at the smart grid’s edge and (2) improving the privacy of video streaming.

Speaker Bio Romaric Duvignau is an Associate Professor in the Distributed Computing and Systems research group at Chalmers University of Technology, Gothenburg, Sweden. His research focuses on designing data-driven algorithms for distributed applications, addressing challenges in fault tolerance, cybersecurity, privacy, big data and streaming analytics. His current interests include the efficient monitoring of large-scale distributed systems, intelligent data management and decision making in smart grids, federated learning, programmable networks, and network traffic classification. Prof. Duvignau teaches courses on computer networking, distributed systems, and data-driven support for cyber-physical systems and was awarded the Chalmers Pedagogic Prize 2023.