Hi! I’m Alessandro Sartore, a MSc student in Artificial Intelligence Systems at the University of Trento, with a strong background in Computer Science and a focus on Computer Vision and Edge AI. I love bridging the gap between theoretical research and practical, high-performance industrial applications.
- 🔭 Currently working at: AI4I as a Computer Vision & AI Engineer.
- 📈 Academic focus: Artificial Intelligence, Edge Computing, and Optimization.
- 🏋️ Beyond the code: I’m a fitness enthusiast who enjoys diving into papers about hypertrophy and nutrition. When I'm not training, I’m listening to Twenty One Pilots or practicing the piano.
- ⚡ Fun fact: I treat my code optimizations with the same discipline I apply to my training sessions: efficient, robust, and always improving!
- AI Video Detection: Generalizing to SOTA Generators
- Proto-CLIP with Generalized vMF Loss
- Face Blurring & Detection Analysis
- Autonomous Software Agents
- CardGraderAI v2.0 Pro
- Schönblick Bellavista - Alpine Hotel Web Experience
- Schönblick Reservation Manager (RAG-Enabled)
- ScorlenTicket - Theatrical Event Booking Platform
- ScorlenTicket - Database Management Dashboard
- Master's Thesis: Edge-Based Anomaly Detection: Optimizing PatchCore for Industry 4.0
- Bachelor's Thesis: Multi-Agent Path Finding (MAPF) Analysis
🎯 What it is
A systematic research project and deep learning pipeline designed to detect artificially generated videos from State-of-the-Art (SOTA) models, such as OpenAI Sora and Pyramid Flow. It leverages 3D Vision Transformer (3D-ViT) architectures to identify temporal inconsistencies when spatial details are too photorealistic to trust. Is the video below real or fake? This project aims to answer that question with high accuracy.
👨💻 What I did I developed a Multi-Scale Temporal architecture to bridge the generalization gap toward unseen generators. By analyzing video clips at different frame rates, I focused the model on capturing both high-frequency flickering and long-term dynamic inconsistencies, achieving a 97.53% accuracy on SOTA test sets.
✨ Key Features
- Multi-Scale Temporal Sampling: Samples clips at 4, 8, and 16 frames to successfully generalize across different video generation models.
- Wavelet Blending: Implemented Discrete Wavelet Transform (DWT) to swap high-frequency components between real and fake videos, creating "Clean Fakes" to force the model to learn semantic motion rather than simple compression shortcuts.
- Explainability: Conducted Stationary Wavelet Transform (SWT) analysis to uncover how the energy profiles of sophisticated fakes mimic real videos in mid-frequency bands.
🛠️ What I used
🔗 Links
🎯 What it is
A Deep Learning project investigating base-to-novel generalization on the Oxford Flowers102 dataset. This project improves class separation on the hypersphere by implementing a prototype-based classification head with a von Mises-Fisher (vMF) loss formulation, integrated with a frozen CLIP backbone.
👨💻 What I did I worked on the end-to-end reimplementation of Proto-CLIP, introducing a prototype-dispersion regularizer and a confidence-based rejection mechanism. My focus was on improving the Base-Novel trade-off, enabling the model to fall back to zero-shot CLIP when uncertain about Novel classes.
✨ Key Features
- vMF Spherical Loss: Replaced standard Euclidean distances with temperature-scaled cosine similarities modeled on the von Mises-Fisher distribution.
- Inference-Time Rejection: Implemented class-specific confidence thresholds estimated on training data, allowing a fallback to zero-shot CLIP for low-confidence inputs.
- Ablation Studies: Conducted targeted experiments to evaluate the impact of Mix-Up augmentation and prototype-dispersion regularizers on the harmonic mean.
- Mathematical Modeling: Utilized the Bhattacharyya distance coefficient to quantify class overlap on the hypersphere.
🛠️ What I used
🔗 Links
🎯 What it is
A Python-based computer vision project designed to detect faces in images and videos, applying various blurring effects (Gaussian, Median, and Pixelation) for privacy protection and GDPR compliance.
👨💻 What I did I conducted a comprehensive benchmarking study comparing the classic Viola-Jones algorithm with the modern YOLO architecture. I compiled OpenCV 3.4 from source to custom-train a Viola-Jones cascade classifier and evaluated both models on the WIDER FACE dataset to measure precision, recall, IoU, and inference speed.
✨ Key Features
- Algorithm Benchmarking: Analyzed the trade-off between the fast execution of Viola-Jones and the high accuracy of YOLO in complex environments.
- Custom Model Training: Successfully trained a custom Haar cascade classifier from scratch to detect faces.
- Privacy Filters: Implemented multiple anonymization techniques, including natural-looking Gaussian blur and high-security Median blur.
- Interactive Pipeline: Developed a Jupyter Notebook providing a step-by-step guide to loading models and applying real-time video blurring.
🛠️ What I used
🔗 Links
🎯 What it is
This project was developed for the Autonomous Software Agents course at the University of Trento and implements autonomous agents using the Belief-Desire-Intention (BDI) model. The agents operate within a simulated Deliveroo.js environment to efficiently pick up and deliver parcels.
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👨💻 What I did I collaborated to build both a standalone agent and a multi-agent system. For the single agent, I worked on the logic to independently explore the environment, update beliefs, and make decisions. For the multi-agent system, I helped implement coordination strategies allowing two agents to share information, divide tasks, and complete deliveries effectively.
✨ Key Features
- BDI Architecture: Developed a system that allows agents to perceive their environment, form goals, and dynamically plan actions.
- Multi-Agent Coordination: Implemented communication handlers and area managers for task division and handover coordination.
- Advanced Planning: Integrated a PDDL planner and custom pathfinding logic to optimize parcel selection and delivery strategies.
🛠️ What I used
🔗 Links
🎯 What it is
An enterprise-grade open-source web application designed for TCG collectors (Pokémon, Yu-Gi-Oh!, Magic: The Gathering). Powered by advanced Computer Vision, it simulates condition grades for major authentication companies (PSA, BGS, CGC) and calculates post-grading ROI.
👨💻 What I did I engineered an end-to-end full-stack platform featuring a high-performance FastAPI backend and a responsive mobile-first frontend. I developed an advanced Computer Vision pipeline utilizing OpenCV for automatic 4-corner perspective warping, precision margin analysis, high-resolution 400% corner inspection, and high-pass spatial frequency filtering to generate defect heatmaps.
✨ Key Features
- Perspective Correction & Warping: Automatic card detection and 750x1050 px rectification on any table surface.
- Defect Heatmap ("X-Ray Vision"): Spatial high-pass filtering and Gaussian absolute difference to highlight surface scratches and edge wear.
- Multi-Company Evaluation Matrix: Cross-evaluates conditions against PSA, BGS, and CGC grading standards with subgrade analysis.
- Multi-Language i18n: Built-in localization supporting 6 languages (EN, IT, ES, DE, FR, JP).
- Modern Tooling: Built with Python 3.11+, FastAPI, and optimized package management via
uv.
🛠️ What I used
🔗 Links
🎯 What it is
A modern, responsive web platform developed for Schönblick Bellavista, a historic Alpine hotel and restaurant located at 2,060 meters in Val Martello, Italy. The website showcases their unique mountain location, room accommodations, and South Tyrolean culinary offerings, while streamlining digital booking inquiries across multiple languages.
👨💻 What I did I built the entire web experience from the ground up, creating a multilingual, accessible interface. I implemented a robust backend architecture to handle dynamic API integrations, automated email workflows, and cloud-based image storage. I am also currently working on integrating physical IoT hardware to provide real-time views of the Alpine landscape.
✨ Key Features
- Multilingual Support: Fully internationalized (i18n) to support English, German, and Italian, catering to the hotel's diverse Alpine clientele.
- Dynamic Social Feed: Integrated the Instagram Graph API to automatically fetch and display the hotel's live social media feed.
- Cloud Image Management: Utilizes Cloudflare R2 buckets for high-performance, optimized image serving.
- Automated Communication: Configured Resend for fast, reliable email delivery for inquiries and booking requests.
- Modern UI/UX: Modular and accessible interface crafted with Chakra UI.
🛠️ What I used
🚧 Still in progress
- Alpine Live View: Connecting a Raspberry Pi with a camera to the server to provide users with a live view of the Val Martello landscape directly on the website.
🔗 Links
🎯 What it is
An intelligent full-stack administrative dashboard built for Schönblick Bellavista to visualize and manage booking reservations. It features a RAG (Retrieval-Augmented Generation) system powered by Google Gemini, allowing hotel hosts to query their reservation database in real time using natural language to retrieve guest data, booking trends, and financial metrics.
⚠️ Note: The data displayed in the images is not real and has been used solely for demonstrative purposes to maintain client privacy.
👨💻 What I did
I engineered an end-to-end reservation management system connecting an interactive Gantt calendar with a secure MongoDB Atlas backend. I developed an automated Web Inquiry & Drafts pipeline that seamlessly integrates with the hotel website (BellaVista), routing incoming multilingual booking requests directly into an admin approval inbox. Furthermore, I built a real-time AI RAG Assistant (Google Gemini) for natural language database querying and designed an operational print summary system for weekly dinner planning.
✨ Key Features
- Gantt Room Allocation Grid: Interactive timeline view for managing 15 hotel rooms & apartments with real-time overlap validation, occupancy statistics, and category filtering (Single, Double, Triple, Apartments).
- AI RAG Assistant (Google Gemini): Natural language database search interface enabling hotel staff to execute real-time MongoDB queries (e.g., "Who arrives today?", "July revenue", "Show notes for John Smith").
- Automated Web Drafts & Multilingual Booking Integration: Direct API sync with the hotel website (
BellaVista), auto-detecting language (EN, DE, IT) and board type (B&B vs. Half Board) with a 1-click allocation workflow. - Weekly Operations & Dinner Count Engine: Automated print summary system calculating daily guest counts, half-board vs B&B breakdown for dinner planning, and operational summaries.
- Full CRUD & Overlap Guard: Complete reservation management with multi-room booking capability, extra bed toggles, and instant conflict checks.
🛠️ What I used
🎯 What it is
A comprehensive full-stack ticketing platform created for theatrical events in the Trentino region (specifically Fai della Paganella and Trento). It provides a seamless flow for users to browse events, select their exact seats, process secure payments, and receive digital tickets via email.
👨💻 What I did I built both the frontend and backend architectures from scratch. I designed an interactive user interface for seat selection and managed the complex backend logic required to handle real-time database updates for bookings, secure financial transactions, and automated ticket dispatching.
✨ Key Features
- Interactive Seat Selection: Developed a dynamic frontend interface allowing users to visually pick their preferred seats for theater layouts.
- Secure Payments: Integrated the Stripe API to handle safe and reliable checkout processes and financial transactions.
- Real-time Reservation Management: Utilized MongoDB to store and manage event details, seat availability, and user bookings effectively.
- Automated Ticketing: Implemented a backend mailing service using standard Gmail integration to automatically generate and dispatch tickets to users upon successful payment.
🛠️ What I used
🔗 Links
🎯 What it is
An administrative web portal designed to manage the backend operations for ScorlenTicket. It provides a secure interface for administrators to oversee theatrical events, manage venue layouts, track real-time bookings, and perform comprehensive CRUD operations on the database.
👨💻 What I did I built a secure, role-based administrative dashboard that acts as the control center for the ticketing platform. I implemented user authentication to ensure protected access and developed intuitive management tools to handle event lifecycle, seating configuration, and reservation oversight.
✨ Key Features
- Secure Admin Authentication: Implemented login protection to restrict backend access to authorized personnel only.
- Full CRUD Functionality: Developed interfaces to create, read, update, and delete event data, seat availability, and venue information.
- Reservation Oversight: Provided a centralized view to monitor, manage, and edit bookings stored in MongoDB.
- Intuitive UI/UX: Built with React and Chakra UI to ensure a clean, efficient workflow for administrators handling high volumes of data.
🛠️ What I used
🔗 Links
🎯 What it is
A high-performance industrial inspection ecosystem designed for real-time defect detection on production lines. The system utilizes a dual-stage architecture—combining unsupervised Anomaly Detection and supervised Deep Learning Classification—to localize and identify manufacturing defects with high precision on standard edge hardware.
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👨💻 What I did I engineered a production-grade inspection pipeline by integrating Anomalib for unsupervised anomaly localization and MobileNetV2 for supervised defect classification. I decoupled the training environment from the inference engine by exporting models into ONNX format and optimizing them with OpenVINO, enabling real-time deployment on edge CPUs without the need for dedicated GPUs. I also developed a custom Streamlit-based GUI to manage inspection recipes, calibrate threshold-based anomaly detection via 3-Sigma statistics, and visualize defect instances using real-time pixel-level segmentation.
✨ Key Features
- Dual-Stage Pipeline: Implemented an anomaly-driven architecture where Anomalib identifies potential defects, followed by a classifier that labels them, eliminating the need for dense pixel-level annotation.
- Edge-Ready Optimization: Leveraged ONNX Runtime and OpenVINO to maximize CPU inference performance, achieving sub-second latency suitable for high-speed production lines.
- Framework Agnostic: Utilized Anomalib to easily experiment with multiple SOTA anomaly detection algorithms (e.g., PatchCore, Padim) within the same pipeline.
- Dynamic Class Visualization: Implemented custom color-coded masks for instance segmentation, allowing operators to distinguish between different defect types instantly.
- Real-time Data Streaming: Integrated ZMQ-based high-speed camera streaming for continuous inspection and automated logging of defect events.
🛠️ What I used
🎯 What it is
An award-winning solution designed to combat the spread of invasive crab species. The project provides an end-to-end platform featuring real-time detection, intuitive reporting, and data visualization.
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👨💻 What I did I developed the complete product stack, covering both frontend and backend development. My primary technical focus was on architecting the Computer Vision pipeline and optimizing deep learning models for deployment on edge hardware.
✨ Key Features
- End-to-End Stack: Engineered the full application, including the user-facing interface, backend services, and database integration.
- Edge AI Pipeline: Designed and implemented a high-performance vision pipeline using YOLO-Nano, optimized for real-time inference on resource-constrained edge devices.
- Hardware-Aware Optimization: Applied model quantization and technical optimizations to maximize throughput while maintaining a minimal memory footprint.
- Recognition & Analytics: Delivered a robust system capable of real-time recognition with high accuracy, ensuring reliable field data.
- Award-Winning Innovation: Recognized as a winner at the VenetoStars 2026 competition for technological excellence in environmental monitoring.
🛠️ What I used
🔗 Links
🎯 What it is
An end-to-end industrial visual inspection system engineered for Zero-Defect Manufacturing on constrained Edge AI hardware. Designed for high-speed continuous cable production at WTM, it accelerates the state-of-the-art PatchCore anomaly detection model for real-time inference on the AMD Kria KV260 SOM and DeepX DX-M1 NPU.
👨💻 What I did I dismantled the computational bottlenecks of PatchCore's memory bank to transition it from a laboratory model to a production-ready edge application. My optimizations focused on three main axes: topological search approximation, boundary-aware coreset compression, and hardware-aware compilation. I also built a dockerized GUI with Streamlit and FastAPI featuring automated 3-sigma threshold calibration, enabling non-expert factory operators to train and deploy models seamlessly.
✨ Key Features
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Topological Search Approximation: Replaced exact linear k-NN search with Hierarchical Navigable Small World (HNSW) graphs, reducing search complexity from linear to
$O(\log m)$ . - Hard Prototype Mining (HPM): Engineered a novel subsampling algorithm targeting "hard" prototypes near the decision boundary, compressing the memory bank to just 5% of its original size while preserving accuracy.
- Hardware-Aware Compilation: Orchestrated PyTorch-to-ONNX conversion, INT8 Post-Training Quantization, and aggressive Operator Fusion specifically for the DeepX NPU's systolic array.
📈 Measurable Impact
- Transformed a theoretically brilliant but unfeasible baseline (0.7 FPS, >1.5 GB RAM) into a highly efficient production system.
- Primary Solution: Achieved an optimal 24.81 FPS and capped dynamic RAM at 125 MB, delivering a 31x speedup over the baseline while maintaining an Image AUROC of 0.9793 on proprietary datasets.
- High-Speed Alternative: Developed a Reverse Distillation architecture that completely eliminates the memory bank, reaching 66.2 FPS (55 MB footprint) for extreme line speeds.
🛠️ What I used
🔗 Links
🎯 What it is
A comprehensive study and benchmarking of state-of-the-art Multi-Agent Path Finding algorithms (such as CBS, ICR, ICTS, and
$X^*$ ), applied to a real-world industrial warehouse scenario to optimize collision-free routing for autonomous agents.
👨💻 What I did
I integrated the advanced
✨ Key Features
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Algorithm Integration: Implemented the Windowed Anytime Multiagent Planning Framework (WAMPF) and
$X^*$ solver in an existing C++ codebase, managing complex data structures and memory states. - Industrial Benchmarking: Evaluated algorithm performance on a topological graph representing a real robotic warehouse with hundreds of nodes and varying numbers of agents.
- Automated Data Pipeline: Built Python scripts to parse experimental data from Excel files and generate survival and scatter plots for an in-depth comparative analysis.
🛠️ What I used
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I'm always open to discussing new projects, AI research, or simply chatting about fitness and music.
"Optimization is the key to both code and training."
Alessandro Sartore
📫 Let's connect!