Portfolio item number 1
This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.
This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.
This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML.
You can find a simple Pokemon squad builder at this link. Made for fun using Claude Code.
Published in SIAM Journal on Imaging Sciences, 2026
Recommended citation: Christian Daniele, Silvia Villa, Samuel Vaiter, and Luca Calatroni, "Deep Equilibrium Models for Poisson Imaging Inverse Problems via Mirror Descent." SIAM Journal on Imaging Sciences, 19(2), pp. 1077–1109, 2026.
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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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Published in Arxiv preprint, 2026
Recommended citation: Jonathan Chirinos-Rodríguez, Christian Daniele, Cédric Févotte, and Emmanuel Soubies, "On The Linear Convergence of Bregman Proximal Gradient Methods with Applications to Kullback–Leibler regression." arXiv preprint arXiv:2607.05539, 2026.
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Published:
Presented my ongoing PhD work at SIAM Chapters meeting for Young Researchers in Milan. PDF of the slide is available on this website,here.
Published:
Published:
Presented my work at EUROPT 2026, at JKU in Linz. You can find my slides here.
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.
Instructors:
| Day | Theoretical Lectures | Practical Labs |
|---|---|---|
| Day 1 | Introduction to Inverse Problems, ill-posedness, spectral filtering, and Bayesian approaches | — |
| Day 2 | Computed Tomography (CT), Radon transform, and basic inversion methods | Lab: Forward models and basic inversion for CT |
| Day 3 | Exemplar inverse problems, model-based regularization, and denoisers | Lab: Tikhonov regularization and denoising-based models for CT |
| Day 4 | Introduction to convex optimization, proximal operators, and Plug-and-Play (PnP) methods | Lab: Utilizing denoisers as proximal operators |
| Day 5 | Algorithm unrolling for CT, bilevel optimization, and Deep Equilibrium Models (DEQs) | Lab: Unrolling and Plug-and-Play methods applied to CT |