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๐Ÿš€ Iโ€™m always open to collaborate, exchange ideas or just talk about anything!

๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป Iโ€™m eager to work with anyone who has great ideas, wants to learn more and more and also share their experience to others. Donโ€™t hesitate to write me if youโ€™d like to propose your help or ask for mine on a project, research, paper-idea, or a moonshot youโ€™re cooking up.

๐Ÿ‘‰ Email Me โœ‰๏ธ


PartecipationsAndTalks

portfolio

projects

MoonBot Navigation

Autonomous lunar rover navigation and interaction โ€” winner of the TESP 2025 Competition.

RoboMAT

MATLAB library for robotics simulations, kinematics, dynamics, control, and path planning.

UniDrive: University Carpooling App

A Flutter/Dart mobile app that connects university students for ride-sharing โ€” schedule, match, and split commutes within the campus community.

publications

Heterogeneous Sheaf Neural Networks

Published in arXiv preprint arXiv:2409.08036, 2024

HetSheaf is a cellular-sheaf framework for heterogeneous graphs that encodes node and edge types through type-aware local feature spaces and learned restriction maps โ€” without specialised architectural components. The companion SheafPool readout is invariant to basis changes and enables graph-level prediction. Gains of up to +2 pp on the Heterogeneous Graph Benchmark with up to 10ร— fewer parameters.

Recommended citation: Braithwaite, L.; Borgi, A.; Onorato, G.; Tarantelli, K.; Restuccia, F.; Silvestri, F.; Liรฒ, P. (2024). "Heterogeneous Sheaf Neural Networks." arXiv:2409.08036.
Go to the Webpage | Download Paper | Download Bibtex

Z-SASLM: Zero-Shot Style-Aligned SLI Blending for Latent Manipulation

Published in CVPR (Computer Vision and Pattern Recognition) 2025 Workshops (Nashville, USA ๐Ÿ‡บ๐Ÿ‡ธ), 2025

Z-SASLM introduces a zero-shot, fine-tuning-free approach to style alignment in diffusion models by blending multiple reference styles directly in latent space using spherical linear interpolation (SLI) with learned, context-aware weights. The method avoids model retraining, preserves content semantics, and yields consistent style transfer across prompts and seeds.

Recommended citation: Borgi, A.; Maiano, L.; Amerini, I. (2025). "Z-SASLM: Zero-Shot Style-Aligned SLI Blending for Latent Manipulation." CVPR 2025 Workshops.
Go to the Webpage | Download Paper | Download Poster | Download Bibtex | GitHub Code

Remember to Forget: Gated Adaptive Positional Encoding

Published in arXiv preprint arXiv:2605.10414, 2026

GAPE (Gated Adaptive Positional Encoding) addresses core limitations of RoPE in long-context language models. A content-aware bias is injected directly into attention logits while preserving rotary geometry: query-dependent and key-dependent gates suppress irrelevant distant tokens while protecting salient context, improving attention sharpness and long-context performance on retrieval and standard benchmarks.

Recommended citation: Ali, R.; Borgi, A.; Irwin, C.; Severino, M.; Liรฒ, P. (2026). "Remember to Forget: Gated Adaptive Positional Encoding." arXiv:2605.10414.
Go to the Webpage | Download Paper | Download Bibtex

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.