معرفی
Andrea Simonetto is a Research Professor at the Applied Mathematics Unit (UMA), ENSTA Paris, Institut Polytechnique de Paris. His work spans optimization, control theory, and learning algorithms for large-scale and streaming data, with applications in smart grids, intelligent transportation, personalized health, and quantum computing.
- Current research focuses on online algorithms for time-varying optimization, personalized optimization for cyber-physical systems, and variational quantum algorithms.
- Past contributions include theoretical and algorithmic advances in convex/non-convex optimization, distributed optimization (robotic networks, smart grids), and signal processing for sparse reconstructions and parallel computing in particle filtering.
Key application domains include renewable energy integration, quantum state preparation, and human-in-the-loop control systems. His research is published in journals like ACM Transactions on Quantum Computing, IEEE Control Systems Letters, and Automatica.
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