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projects

publications

Self-Tuning Regularization for Image Scanning Microscopy

Published in Arxiv preprint, 2026

Recommended citation: Sofia Agostoni, Lisa Cuneo, Christian Daniele, Giacomo Garré, Laurent Le, Alessandro Zunino, Giuseppe Vicidomini, and Luca Calatroni, "Self-Tuning Regularization for Image Scanning Microscopy." arXiv preprint arXiv:2605.31426, 2026.
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talks

Presentation at SIAM OP26

Published:

Attended and presented at SIAM OP26, at University of Edinburgh, in the mini-symposium on Convergence Guarantees for Data-driven Methods in Inverse Problems organized. My slides are available on this website, here.

teaching

Computational imaging & learning: from physics to data-driven methods

PhD & Master Course, Universidad de la República (UdelaR), Facultad de Ingeniería, 2026

Intensive one-week course targeting PhD candidates and Master’s students in Mathematics, Engineering, and Computer Science.

  • Theory & Lectures: Covered the mathematical foundations of physics-based inverse problems, forward modeling, and the transition toward data-driven and hybrid reconstruction methods.
  • Hands-on Labs: Designed and executed practical Python notebooks focusing on optimization algorithms, deep learning architectures for imaging, and inverse problem solvers.

Instructors:

Course Structure

DayTheoretical LecturesPractical Labs
Day 1Introduction to Inverse Problems, ill-posedness, spectral filtering, and Bayesian approaches
Day 2Computed Tomography (CT), Radon transform, and basic inversion methodsLab: Forward models and basic inversion for CT
Day 3Exemplar inverse problems, model-based regularization, and denoisersLab: Tikhonov regularization and denoising-based models for CT
Day 4Introduction to convex optimization, proximal operators, and Plug-and-Play (PnP) methodsLab: Utilizing denoisers as proximal operators
Day 5Algorithm unrolling for CT, bilevel optimization, and Deep Equilibrium Models (DEQs)Lab: Unrolling and Plug-and-Play methods applied to CT