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Publications

See also my Google Scholar profile. Preprints are listed separately below.

2026

  • N. Dal Seno, G. Ciccarese, D. Evangelista, E. Loli Piccolomini, A. Corsini, M. Berti, Rapid landslide mapping during the 2023 Emilia-Romagna disaster: assessing automated approaches with limited training data, Natural Hazards and Earth System Sciences, 2026, https://doi.org/10.5194/nhess-26-4101-2026;

  • D. Evangelista, P. Cascarano, E. Loli Piccolomini, A diffusion-based generative prior approach to sparse-view computed tomography, Computerized Medical Imaging and Graphics, 2026, https://doi.org/10.1016/j.compmedimag.2026.102802;

  • E. Morotti, D. Evangelista, A. Sebastiani, E. Loli Piccolomini, Adaptive Weighted Total Variation Boosted by Learning Techniques in Few-View Tomographic Imaging, Journal of Scientific Computing, 2026, https://doi.org/10.1007/s10915-026-03199-7;

  • F. Merizzi, D. Evangelista, H. Loukos, Controlling ensemble variance in diffusion models: an application for reanalyses downscaling, Neural Computing and Applications, 2026, https://doi.org/10.1007/s00521-025-11709-1;

2025

  • D. Evangelista, E. Morotti, S. Colombo Serra, P. Luo, G. Valbusa, D. Bianchi, LIP-CAR: a learned inverse problem approach for medical imaging with contrast agent reduction, MICCAI 2025, https://doi.org/10.1007/978-3-032-05141-7_36;

  • D. Bianchi, D. Evangelista, S. Aleotti, M. Donatelli, E. Loli Piccolomini, W. Li, A data-dependent regularization method based on the graph Laplacian, SIAM Journal on Scientific Computing, 2025, https://doi.org/10.1137/23M162750X;

  • E. Loli Piccolomini, D. Evangelista, E. Morotti, Deep Guess acceleration for explainable image reconstruction in sparse-view CT Computational Medical Imaging and Graphics, 2025, https://doi.org/10.1016/j.compmedimag.2025.102530;

  • S. Marro, D. Evangelista, X.A. Huang, E. La Malfa, M. Lombardi, M.J. Wooldridge, Language Models Are Implicitly Continuous, Thirteenth International Conference on Learning Representations (ICLR2025), 2025, https://openreview.net/forum?id=SMK0f8JoK;

  • D. Evangelista, E. Loli Piccolomini, E. Morotti, J.G. Nagy, To Be or Not to Be Stable, That Is the Question: Understanding Neural Networks for Inverse Problems, SIAM Journal on Scientific Computing, 2025, https://doi.org/10.1137/23M1586872;

  • E. Morotti, D. Evangelista, E. Loli Piccolomini, Robust Non-convex Model-Based Approach for Deep Learning-Based Image Processing, Numerical Computations: Theory and Algorithms (NUMTA 2023 proceedings), 2025, https://doi.org/10.1007/978-3-031-81241-5_30;

  • G.V. Spinelli, D. Evangelista, L. Hu, F. Zama, Neural network-based inversion of NMR dispersion profiles for enhanced analysis of food systems, Neural Computing and Applications, 2025, https://doi.org/10.1007/s00521-024-10859-y;

2024

  • N. Dal Seno, D. Evangelista, E. Loli Piccolomini, M. Berti, Comparative analysis of conventional and machine learning techniques for rainfall threshold evaluation under complex geological conditions, Landslides, 2024, https://doi.org/10.1007/s10346-024-02367-w;

  • E. Morotti, F. Merizzi, D. Evangelista, P. Cascarano, Inpainting with style: forcing style coherence to image inpainting with deep image prior, Frontiers in Computer Science 6, 2024, https://doi.org/10.3389/fcomp.2024.1478233;

  • D. Evangelista, Regularization meets GreenAI: a new framework for image reconstruction in life sciences applications, PhD Thesis, 2024;

2023

  • A. Asperti, D. Evangelista, S. Marro, F. Merizzi, Image embedding for denoising generative models, Artificial Intelligence Review, 2023, https://doi.org/10.1007/s10462-023-10504-5;

  • D. Evangelista, E. Morotti, J. Nagy, E. Loli Piccolomini, Ambiguity in solving imaging inverse problems with deep learning based operators, Journal of Imaging, 2023, https://doi.org/10.3390/jimaging9070133;

  • D. Bianchi, M. Donatelli, D. Evangelista, W. Li, E. Loli Piccolomini, Graph Laplacian and Neural Networks for Inverse Problems in Imaging: GraphLaNet, International Conference on Scale Space and Variational Methods in Computer Vision, 2023, http://doi.org/10.1007/978-3-031-31975-4_14;

2022

2021

  • E. Morotti, D. Evangelista, E. Loli Piccolomini, A green prospective for learned post-processing in sparse-view tomographic reconstruction, Journal of Imaging, 2021, https://doi.org/10.3390/jimaging7080139;

  • A. Asperti, D. Evangelista, M. Marzolla, Dissecting FLOPs along input dimensions for GreenAI cost estimations, International Conference on Machine Learning, Optimization, and Data Science, 2021, https://doi.org/10.1007/978-3-030-95470-3_7;

  • A. Asperti, D. Evangelista, E. Loli Piccolomini, A survey on Variational Autoencoders from a GreenAI perspective, SN Computer Science, 1st March 2021, www.doi.org/10.1007/s42979-021-00702-9;

Preprints

2026

  • E. Morotti, D. Evangelista, E. Loli Piccolomini, Consecutive Posterior Fusion for Diffusive Recovery of Unobservable Image Structures, arXiv preprint, 2026, https://arxiv.org/abs/2610.03261;

  • D. Evangelista, Physics-Guided Flow Matching for CT Image Reconstruction, arXiv preprint, 2026, https://arxiv.org/abs/2608.28256;

  • B. Raimondi, D. Evangelista, M. Gabbrielli, E. Loli Piccolomini, D-Score: A Spectral Hidden-State Signal for Hallucination Detection in Large Language Models, arXiv preprint, 2026, https://arxiv.org/abs/2607.24586;

  • D. Evangelista, E. Morotti, F. Pivi, M. Gabbrielli, Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration, arXiv preprint, 2026, https://arxiv.org/abs/2605.12573;

  • I. Marasco, D. Evangelista, E. Loli Piccolomini, M. Colajanni, CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments, arXiv preprint, 2026, https://arxiv.org/abs/2603.17548;

  • B. Raimondi, F. Pivi, D. Evangelista, M. Gabbrielli, The CompMath-MCQ Dataset: Are LLMs Ready for Higher-Level Math?, arXiv preprint, 2026, https://arxiv.org/abs/2603.03334;

2025