Date of the expedition
From 15/09/2023to 14/03/2024
Robust X-Haul Management Based on Uncertainty-aware ML
I am a final-year PhD student in Telecommunications at Politecnico di Milano, Italy, under the supervision of Prof. Massimo Tornatore. Before that, I got my B.Sc. and M.Sc. in Telecommunications Engineering at University of Bologna, Italy. My work is mostly focused the integration of Machine Learning with communications network optimization, management, and control. Specifically, I am researching how to design intelligent agents that can learn to optimize a communication network, and how do they compare with classical optimization techniques. I enjoy skiing, hiking, tennis, and video games.
The results of this research project will be submitted for publication in a high-impact conference or journal. The associated software will be also publicly provided to the research community with an open-source license, potentially enabling downstream industrial impact. Finally, I expect the outcome of my expedition to spark a long-lasting collaboration between Politecnico di Milano and Columbia University.
Impact of the Fellowship
I expect the outcome of this project to be impactful in several orthogonal aspects, namely:
- Joint publication of the main results in a high-impact conference/journal,
- Publication of the research artifacts (e.g., software, and possibly data) associated to the project with a permissive open-source license to facilitate downstream industrial impact,
- Sparking a long-lasting line of cooperation between Politecnico di Milano and Columbia University, potentially leading to other student exchange programs,
- Opening up joint applications to further research funding opportunities,
- Starting a new line of research on X-Haul network management at Politecnico di Milano, resulting in both joint publications with Columbia University and independent projects, involving first-year PhD and final-year Master students,
- Development of innovative teaching material in Next-Generation Internet subjects based
on remote experimentation on US hardware testbeds and community networks.