Projects
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OPTItree: advanced models, methods, and algorithms for phylogenetic estimation
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MSCA Postdoctoral Fellowship (European Fellowship), Venice School of Management, Ca' Foscari University of Venice, with the Centre National de la Recherche Scientifique (CNRS), France, as partner organization (2026-2028)
OPTItree aims to make mathematical models for computational phylogenetics — the field that reconstructs evolutionary trees from DNA and genomic data — scale to much larger datasets while keeping either a formal guarantee of correctness or a certified bound on solution quality. Reconstructing the correct evolutionary tree matters in sensitive settings such as tracking pandemics or studying how tumors evolve, but the exact optimization problems involved are so computationally demanding that current tools fall back on heuristics with no such guarantees. The project studies the polyhedral structure of the balanced minimum evolution criterion — a statistically consistent, exact approach to the problem — with two goals: pushing the size of instances that can be solved exactly well beyond the current state of the art, and designing approximation algorithms with certified quality bounds for the cases where exact methods remain out of reach.
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Short-term prediction of NO2 concentration on ground-based weather sensors data in urban scale area with graph neural networks
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Carlo Milesi, Francesco Pisanu, and Carlino Casari (2022-23)
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Cagliari 2020
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Italian Ministry of Research (MIUR) Fellow — Traffic flow forecasting with neural networks, CRS4, Italy. Collaborator. (2019-2020)
Cagliari 2020 was an industrial research project within the Smart Cities program, led by Vitrociset in partnership with CRS4, the DIEE department of the University of Cagliari, the INFN Cagliari division, and Space SpA, with the involvement of the local public transport operator CTM and the patronage of the City of Cagliari. The project developed tools to optimize urban mobility in the greater Cagliari area by combining real-time vehicle and traffic data with intelligent transport systems (ITS), aiming to improve public and private traffic flow and reduce emissions, and to provide public administrations with data-driven decision-support tools. My contribution focused on forecasting traffic flow using neural networks.