Posts by Collection


🚀 I’m always open to collaborate, exchange ideas or just talk about anything!

👨🏻‍💻 I’m eager to work with anyone who has great ideas, wants to learn more and more and also share their experience to others. Don’t hesitate to write me if you’d like to propose your help or ask for mine on a project, research, paper-idea, or a moonshot you’re cooking up.

👉 Email Me ✉️


PartecipationsAndTalks

portfolio

projects

MoonBot Navigation

Autonomous lunar rover navigation and interaction, winner of the TESP 2025 Competition.

RoboMAT

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

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, 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 10× fewer parameters.

Recommended citation: Braithwaite, L.; Borgi, A.; Onorato, G.; Tarantelli, K.; Restuccia, F.; Silvestri, F.; Liò, 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 🇺🇸), 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.; Liò, P. (2026). "Remember to Forget: Gated Adaptive Positional Encoding." arXiv:2605.10414.
Go to the Webpage | Download Paper | Download Bibtex

Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs

Published in arXiv preprint arXiv:2608.28853, 2026

ESNN learns directed, matrix-valued transport between neighbouring vector features while preserving exact Euclidean equivariance. It captures radial and tangential geometric interactions, supports controlled symmetry relaxation, and improves performance across dynamics, mesh simulation, point clouds, and molecular-property prediction.

Recommended citation: Borgi, A.; Severino, M.; Silvestri, F.; Liò, P. (2026). "Equivariant Sheaf Neural Networks: Learning Geometric Transport on Graphs." arXiv:2608.28853.
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.