| INdAM - GNCS 2024: Deep variational learning: A combined approach for image reconstruction |
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| INdAM - GNCS 2024: Low-rank models and optimization algorithms for data analysis |
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| INdAM - GNCS 2024: Numerical models and methods for image processing |
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| INdAM - GNCS 2023: Data-driven optimization methods: new theoretical and practical perspectives |
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| INdAM - GNCS 2023: Advanced models and methods in computer vision |
| PRIN PNRR 2022: Advanced optimization METhods for automated central veIn Sign detection in multiple sclerosis from magneTic resonAnce imaging (AMETISTA) |
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| PRIN 2022: Numerical optimization with adaptive accuracy and applications to machine learning |
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| PRIN 2022: Sustainable Tomographic Imaging with Learning and rEgularization (STILE) |
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| PRIN 2022: Inverse Problems in the Imaging Sciences (IPIS) |
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| PRIN 2022: Inverse problems in PDE: theoretical and numerical analysis |
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| INdAM - GNCS 2022: Adaptive optimization for machine learning |
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| INdAM - GNCS 2022: Machine learning and variational techniques for tomography |
| FAR Unimore Mission Oriented 2021: Artificial Intelligence-based Mathematical Models and Methods for low dose CT imaging |
| Programma operativo Fondo sociale europeo 2014/2020: Sviluppo di metodi di ottimizzazione stocastica per applicazioni innovative del Machine Learning |
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| INdAM - GNCS 2020: Numerical optimization in image restoration and reconstruction |
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| INdAM - GNCS 2020: Optimization for machine learning and machine learning for optimization |
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| INdAM - GNCS 2019: Advanced nonlinear optimization methods for image processing |
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| INdAM - GNCS 2019: Adaptive techniques for optimization methods in machine learning |
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| INdAM - GNCS 2018: Stochastic optimization methods for large scale machine learning problems |
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| INdAM - GNCS 2017: Nonlinear numerical methods for inverse problems and applications |
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| INdAM - GNCS 2017: Numerical methods for large-scale constrained optimization problems and applications |
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| INdAM - GNCS 2016: New frontiers of nonsmooth optimization in inverse problems |
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| INdAM - GNCS 2015: New regularization issues in imaging |
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| INdAM - GNCS 2015: Numerical methods for nonconvex or nonsmooth optimization and applications |
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| INdAM - GNCS 2014: First-order optimization methods for image reconstruction and analysis |
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| INdAM - GNCS 2014: Lipschitz-independent descent methods for nondifferentiable optimization |
| SPINNER 2013: High-complexity inverse problems in biomedical applications and social systems
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| INdAM - GNCS 2013: Numerical methods and software for large-scale optimization with applications to image processing |
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| INdAM - GNCS 2013: Acceleration of first-order methods for constrained optimization |
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| PRIN 2012: Structured matrices in signal and image processing (contract 2012MTE38N) |
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| FIRB Futuro in Ricerca 2012: Learning meets time: a new computational approach for learning in dynamic systems (contract RBFR12M3AC) |
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| INdAM - GNCS 2011: Optimization and regularization in machine learning |
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| PRRIITT 2010: Development of a modular software platform and related ontology for the creation, management and sharing of technical digital libraries for the mechanical field |
| Tecno-INAF 2010: Exploiting the adaptive power: a dedicated free software to optimize and maximize the scientific output of images from present and future adaptive optics facilities |
| ISCRA 2010: Parallel algorithms for nonlinear optimization in inverse problems |
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| INdAM - GNCS 2010: Imaging techniques from non uniform samplings of the Fourier Transform |
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| INdAM - GNCS 2009: Optimization methods for image reconstruction and inverse problems with sparsity constraints |
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| INdAM - GNCS 2009: Solution of inverse problems in neuroscience and astronomy |
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| PRRIITT 2008: Machine learning algorithms for text categorization |
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| PRIN 2008: Optimization methods and software for inverse problems (contract 2008T5KA4L) |
