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PartecipationsAndTalks

portfolio

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projects

AdaViT (Adaptive Vision Transformers)

Adaptive Vision Transformer with dynamic token sparsification and halting for efficient image classification.

ALPR: Automatic License Plate Recognition System

End-to-end real-time license plate detection and OCR pipeline with dual GUIs, one for security managers, one for drivers, built with PyTorch and Streamlit.

(AMR) Autonomous Mobile Robotics Cleaning Robot

Autonomous mobile robot for indoor cleaning with navigation, obstacle avoidance, and task orchestration.

Archaic Italian Modernization: Historical Italian Rewriting with Transformers and LLM Judges

Automatic modernization of 13th-15th century Italian into modern Italian using multilingual transformers, prompted LLMs, and LLM-as-a-judge evaluation.

AutoDriveCarSimulator: Autonomous Driving with CNNs

A simulation platform for developing and testing autonomous driving algorithms, using CNNs to map raw camera frames to steering and throttle commands.

BioHeat PINNs: Temperature Estimation with Bio-Heat Equation using Physics-Informed Neural Networks

Physics-Informed Neural Networks for real-time temperature estimation via the Pennes Bio-Heat Equation, supporting hyperthermia therapy control.

CareConnect: AI-Driven Hospital Environment Monitoring System

An AI system for querying hospital environmental sensor data via natural language chat, generating real-time graphs, and triggering automated actions via LangChain and MQTT.

Clustering-Deepening: Clustering Algorithms for Object Tracking & Image Segmentation

An in-depth study of clustering algorithms, from k-Means to DBSCAN and GMMs, applied to object tracking and image segmentation.

ElectricCompany-TicketingSystem: IT Infrastructure & Ticketing for an Electric Consultancy

End-to-end analysis and implementation of IT infrastructure (disaster recovery, smart working, fleet management) and a full ticketing system for an electric consultancy firm.

EmailSpamDetector: Spam Detection with Bidirectional LSTMs

Classifies spam and ham emails using a Bidirectional LSTM, capturing both forward and backward temporal context in email text for high-accuracy filtering.

HelpDeskSystem: Web-Based Customer Support Platform

A full-stack web help desk for issue tracking and customer support, with ticket management, user authentication, and real-time status updates.

Home-Automation: Smart Home with IoT and Arduino

End-to-end smart home system, from a physical miniature house build to Arduino-powered sensors, automated routines, and a companion mobile app.

InstaSocial: Photo-Sharing Social Platform

A full-stack Instagram-like photo sharing app, upload, explore, like, and comment, built with Vue.js frontend, Go REST API, and Docker deployment.

Java-CategoryTheory: A Category Theory Library in Java

A Java library that models core Category Theory constructs, categories, functors, natural transformations, and demonstrates their practical role in software design.

MLPipelineOptimizationStudy: End-to-End ML Pipeline Exploration

A systematic exploration of ML pipeline optimisation, covering preprocessing, feature engineering, model selection, and hyperparameter tuning across multiple algorithms.

MoonBot Navigation

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

NSIO: Neural Search Indexing Optimization

Optimising the Differentiable Search Index (DSI) with data augmentation and parameter-efficient fine-tuning (LoRA, QLoRA, AdaLoRA), evaluated on MS MARCO.

PC-Performance-Monitoring: Statistical Analysis & ML for System Metrics

Collects, analyses, and visualises PC performance metrics, then applies ML clustering to detect anomalies and performance degradation patterns.

QRCodeGenerator: Custom Static QR Code Generator

Generate static, unlimited-use QR codes with custom styles, embedded icons, and optional captions, entirely in Python.

RealTime-VLM: Real-Time Vision-Language Model Inference in the Browser

Browser-based real-time VLM inference, continuously captures webcam frames and feeds them to any OpenAI-compatible vision API with sub-second latency.

RoboMAT

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

RTAD5G: Real-Time Anomaly Detection in 5G Networks

A real-time anomaly detection pipeline for 5G network telemetry, developed in collaboration with Hewlett Packard Enterprise (HPE).

SkinMe: Deep Learning for Skin Disease Detection

A deep learning application that classifies skin conditions from dermoscopic images using CNNs and LSTMs, supporting early diagnosis assistance.

StyleAligned: Zero-Shot Style Alignment in Text-to-Image Generation

A zero-shot framework for consistent style transfer in text-to-image generation, using minimal shared attention to propagate a reference style without fine-tuning.

UniDrive: University Carpooling App

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

XGNNs: Model-level Explanation of Graph Neural Networks with RL through Graph Generation

Model-level explanations for GNNs via reinforcement-learned graph generation on MUTAG.

Z-SASLM: Zero-Shot Multi-Style Image Synthesis via Spherical Linear Interpolation

CVPR 2025 workshop paper, a zero-shot framework for smooth multi-style image synthesis using Spherical Linear Interpolation in the latent space of diffusion models.

publications

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

Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves

Published in arXiv preprint arXiv:2512.00242, 2025

ArXiv preprint on Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves.

Recommended citation: Borgi, A.; Silvestri F.; Liò P. (2025). "Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves.
Go to the Webpage | Download Paper | Download Bibtex

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

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 — from label noise to the new attack surfaces of AI-generated code and agents.

Recommended citation: Yu, T.; Wang, J.; Li, M.; Hu, Y.; Hu, J.; Cai, X.; Hu, W.; Borgi, A. (2026). "Program Graph Learning for Software Vulnerability Analysis: A Survey." Transactions on Graph Intelligence and Network Applications. https://doi.org/10.53941/tgina.2026.100005.
Go to the Webpage | Download Paper

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.

Recommended citation: Ali, R.; Borgi, A.; Severino, M.; Gravina, A.; Bacciu, D.; Liò, P.; Irwin, C. (2026). "Let the Heads Talk: Beyond Diagonal Graph Attention." arXiv:2610.01494.
Go to the Webpage | Download Paper | Download Bibtex

Oscillatory Neural Dynamics over Sheaves

Published in arXiv preprint arXiv:2610.10018, 2026

ONDA combines learned matrix-valued sheaf transport with second-order oscillatory dynamics for long-range graph learning. A stalk-wise sensitivity analysis characterises persistent influence in the conservative setting, while experiments demonstrate strong performance on long-range propagation, severe graph bottlenecks, and graph transfer, alongside competitive results on heterophilic benchmarks.

Recommended citation: Van Looy, J.-W.; Trenta, A.; Gravina, A.; Borgi, A.; Zanchetta, F.; Liò, P.; Bacciu, D.; Fioresi, R. (2026). "Oscillatory Neural Dynamics over Sheaves." arXiv:2610.10018.
Go to the Webpage | Download Paper | Download Bibtex

Heterogeneous Sheaf Neural Networks

Published in NeurReps Workshop @ NeurIPS 2026 - Symmetry and Geometry in Neural Representations, (Sydney, Australia 🇦🇺), 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 10× fewer parameters.

Recommended citation: Braithwaite, L.; Borgi, A.; Onorato, G.; Tarantelli, K.; Restuccia, F.; Silvestri, F.; Liò, P. (2026). "Heterogeneous Sheaf Neural Networks." NeurIPS 2026 Workshop on Symmetry and Geometry in Neural Representations (NeurReps). arXiv:2409.08036.
Go to the Webpage | Download Paper | Download Bibtex

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