Francisco Rau (DC4)

20250428_203036 - Francisco Rau
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Efficient MAC scheduling for ultra-dense wireless sensor networks based on 802.11 standard

Current work: Develop traffic modeling approaches aimed at effectively implementing artificial intelligence techniques, specifically for network traffic classification. Applying AI-driven embedded systems techniques, emphasizing real-time data processing and hardware acceleration strategies to achieve optimal performance. Additionally, I am actively exploring innovative incremental learning methods to dynamically adapt and enhance the capabilities of existing models. A key objective of my research is to seamlessly integrate these AI techniques into MAC scheduling frameworks, specifically tailored for ultra-dense network environments, with the goal of significantly improving WiFi network efficiency and performance.

Supervisor: Dr. Carlos Herranz

Co-Supervisors: Dr. Iñaki Val, Prof. Joaquin Perez Soler

List of Publications

  1. F. Rau, P. Georgieva, C. Herranz, I. Val and J. Perez, “Incremental Learning in Network Traffic Management: A Fixed-Representation Rehearsal Approach,” 2026 15th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Edinburgh, United Kingdom, 2026, pp. 1-6, doi: 10.1109/CSNDSP68462.2026.11654486.

Recruited at:

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Enrolled at:

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Background

I hold a B.Eng. degree in Electrical and Electronics Engineering from Universidad de Santiago de Chile in 2009, a B.Sc. degree in Industrial Engineering from Universidad Tecnica Federico Santa Maria in 2017, and M.Sc. degree in Electrical Engineering from Universidad Santiago de Chile in 2023.
I have worked as Satellite Engineer and Project Engineer in Chile, FTTH Engineer in Ecuador. In my last job I worked as a Senior Project Manager, implementing projects such as 5G NSA, SDN Network, MPLS/IP Core Network Expansion at a Telecommunications Service Provider in Chile.