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Presented my work in a talk at SIAM OP26
Published:
This year I had the great opportunity to attend SIAM OP26 conference at University of Edinburgh. I attended very interesting talks on inexact methods and multi-level optimization, and I presented my work in the mini-symposium on Inexact and accelerated methods for nonlinear problems. My slides are available here.
Poster presented at SIAM Chapters meeting in Milan
Published:
Attending this year “SIAM Chapters meeting for Young Researchers” at PoliMi, I had the opportunity to present a poster on my current work on Mixed-Precision Newton’s method. You can find the poster in PDF here.
I uploaded my Master thesis on Hal!
Published:
You can find my master thesis at https://hal.science/tel-05375552.
portfolio
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projects
Pokemon squad builder
You can find a simple Pokemon squad builder at this link. Made for fun using Claude Code.
publications
Comparison of news commonality and churn in international news outlets with TARO
Published in In the proceedings of Proceedings of the 34th ACM Conference on Hypertext and Social Media, 2023
Recommended citation: Giuseppe Carrino, Angelo Di, Gioele Barabucci, "Comparison of news commonality and churn in international news outlets with TARO." In the proceedings of Proceedings of the 34th ACM Conference on Hypertext and Social Media, 2023.
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Publishing, linking and translating news in multilingual communities: a mirror of cultural differences?
Published in In the proceedings of Proceedings of the 35th ACM Conference on Hypertext and Social Media, 2024
Recommended citation: Giuseppe Carrino, Angelo Di, Davide Picca, "Publishing, linking and translating news in multilingual communities: a mirror of cultural differences?." In the proceedings of Proceedings of the 35th ACM Conference on Hypertext and Social Media, 2024.
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Investigating news coverage and circulation over time in a quantitative manner: the TARO framework
Published in New Review of Hypermedia and Multimedia, 2025
Recommended citation: Giuseppe Carrino, Angelo Iorio, Gioele Barabucci, "Investigating news coverage and circulation over time in a quantitative manner: the TARO framework." New Review of Hypermedia and Multimedia, 2025.
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Frugality in second-order optimization: floating-point approximations for Newton’s method
Published in 07, 10, 2025
Recommended citation: Giuseppe Carrino. Frugality in second-order optimization: floating-point approximations for Newton method. Computer Science [cs]. Bologna University, 2025. English. ⟨NNT : ⟩. ⟨tel-05375552⟩
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talks
Poster on ‘Mixed-Precision Newton’s method’ at M2L
Published:
Presented a poster on my ENS internship work at M2L Summer School by Google DeepMind.
Poster on ‘Mixed-Precision Newton’s method’ at SIAM Chapters Meeting
Published:
Presented a poster on my ongoing PhD work at SIAM Chapters meeting for Young Researchers in Milan. Poster PDF is available on this website, here.
Talk at PEQUAN Team meeting
Published:
Attended PEQUAN Team (LIP6) meeting and presented my ongoing work on mixed precision Newton’s method. My slides are available on this website, here.
Presentation at SIAM OP26
Published:
Attended and presented at my work on mixed-precision Newton’s methods at SIAM OP26, at University of Edinburgh, in the mini-symposium on Inexact and accelerated methods for nonlinear problems organized by Coralia Cartis. My slides are available on this website, here.
teaching
‘Travaux dirigés’ of Machine Learning
Master degree course, ENS Lyon, Master's degree in Computer Science, 2026
Teaching to students of first year of Master’s degree in Computer Science at École normale supérieure of Lyon. Exercises on foundations of machine learning, both theoretical, through mathematical proofs, and applied, using Python notebooks. —
Topics covered
- Lesson 1: Empirical vs true risk, Bayes classifier;
- Lesson 2: Conditional expectation, kNN, regresso-gram;
- Lesson 3: Linear Regression, Ordinary Least Squares, Singular Value Decomposition;
- Lesson 4: Logistic Regression, LDA, Gradient Descent, Newton’s method;
- Lesson 5: Clustering, k-Means(++), Hierarchical Clustering, Spectral Clustering;
- Lesson 6: Dimensionality reduction (PCA, Dictionary Learning, Autoencoders);
- Lesson 7: Trees, random forests, ensemble methods;
- Lesson 8: Density estimation, Gaussian Mixture Models;
- Lesson 9: Stochastic Gradient Descent;
- Lesson 10: DL Intro, Convolutional networks, Batch-Norm, Residual Layers;
- Lesson 11: Generative models, Flow Matching.