Alessio Borgi - About
๐ About Me
๐ Open to Collaborate!
I'm eager to work with anyone who has great ideas, wants to learn and share their experience. Don't hesitate to reach out if you'd like to collaborate on a project, research, paper idea, or a moonshot you're cooking up.
๐บ๏ธ Places I've Been
๐ Latest Publications
Heterogeneous Sheaf Neural Networks
Published in NeurReps Workshop @ NeurIPS 2026 - Symmetry and Geometry in Neural Representations, (Sydney, Australia ๐ฆ๐บ), 2026
Accepted at the NeurReps Workshop @ NeurIPS 2026. 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 10ร fewer parameters.
Let the Heads Talk: Beyond Diagonal Graph Attention
Published in arXiv preprint arXiv:2610.01494, 2026
A bridge between attention and sheaf neural networks: Top-A shows that multi-head attention is diagonal matrix-valued transport on its head space and adds edge-conditioned off-diagonal routes, letting each interaction move information across heads before aggregation. It recovers vanilla attention exactly when routing vanishes and improves relational, heterogeneous and algorithmic reasoning.
Program Graph Learning for Software Vulnerability Analysis: A Survey
Published in Transactions on Graph Intelligence and Network Applications (TGINA), Scilight, 2026, 2026
A journal survey of graph-driven vulnerability intelligence: how software vulnerability detection, classification, localization and repair become graph learning problems over program entities (ASTs, CFGs, DFGs, PDGs, CPGs). The survey covers graph-based, multimodal and LLM-assisted methods, the dataset/tool/framework ecosystem, and five open challenges โ from label noise to the new attack surfaces of AI-generated code and agents.
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.
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.
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๐ GitHub Activity
A clean PyTorch / PyG implementation library of Sheaf Neural Networks as Message Passing comprising all variants and a benchmark suite with 14+ Datasets.
Simulation of a Cleaning Robot with the capability of performing SLAM of the environment the robot is navigating, Planning Trajectories to calculate optimal paths considering static and dynamic obstacles, and Dynamic Obstacle Avoidance to detect and navigate around obstacles.
RealTime-VLM brings real-time VLM inference to the browser. It continuously captures webcam frames, sends image+text to an OpenAI-compatible API, and displays responses with sub-second latency. Works with local or hosted VLMs.
Improving hyperthermia treatment by controlling temperature distribution in both 1D and 2D domains and thermal energy applied to cutaneous and subcutaneous tissues, through Bio-Heat equation with Physics-Informed Neural Networks (PINNs).
Autonomous robot for lunar navigation and object interaction, developed during TESP '25 at the Space Robotics Lab (Tohoku University). Features custom robot design, Dijkstra-based path planning, object detection with vision, and gripper control.
A comprehensive MATLAB library for solving a wide range of robotics tasks, providing tools and functions for robotic simulations, control systems, kinematics, and path planning.
[CVPR 2025] Z-SASLM is a zero-shot framework for multi-style image synthesis leveraging Spherical Linear Interpolation (SLI) to achieve smooth, coherent blendingโwithout any fine-tuning.
Explainable Graph Neural Networks (XGNNs) and Reinforcement Learning (RL) techniques for graph generation and optimization tasks. The project aims to explore and validate the use of explainable AI for creating interpretable graph structures.
Adaptive Vision Transformer for efficient image classification, implementing dynamic token sparsification to reduce computational costs while maintaining accuracy.
Novel framework for Zero-Shot Style Alignment in Text-to-Image generation, incorporating Multi-Modal Context-Awareness and Multi-Reference Style Alignment, using minimal attention sharing, ensuring consistent style transfer without fine-tuning.
A deep learning-based application for skin disease detection and classification.
A comprehensive system for detecting and recognizing license plates in real-time, combining image processing, object detection, and optical character recognition (OCR) to provide an accurate and efficient solution for automatic license plate recognition, with also double GUI, both for the Security Manager and for the Car User.