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Currently pursuing a Master's Degree in Artificial Intelligence and Data Engineering at @Unipisa
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ACN_bgp-automation
January 20, 2026 – Present
Project for the "Advanced Computer Networking" class at Pisa University
View ProjectMLP_Verilog
May 9, 2025 – June 28, 2025
Project for the "Symbolic and Evolutionary Artificial Intelligence" class at Pisa University
View Projectlm-rag-techniques
December 19, 2024 – January 25, 2025
Question-Answering (QA) system powered by Retrieval-Augmented Generation (RAG). The system leverages advanced methods such as Rank Fusion and Cascading Retrieval for optimized document retrieval and contextual QA generation.
View Projectir-msmarco-passage
October 5, 2024 – January 25, 2025
Information Retrieval pipeline leveraging TFIDF and BM25 to evaluate ranking accuracy over MSMARCO Passage dataset
View ProjectUniNotes
February 25, 2024 – October 26, 2024
A centralized hub for all my university course notes
View ProjectDSMT-Project
December 15, 2023 – May 20, 2024
A distributed event streaming application for fleet-monitoring and fraud detection.
View ProjectBeatBuddy
November 28, 2023 – June 23, 2025
Project for the "Large-Scale and Multi-Structured Databases" class at Pisa University
View Projectk-means_in_MapReduce
May 22, 2023 – February 26, 2024
Project for the "Cloud Computing" class at Pisa University
View ProjectSmartGreenHouse
May 18, 2023 – June 3, 2023
IoT system for the automation of frequent procedures in a smart greenhouse.
View ProjectAIDE-unipi
June 10, 2022 – Present
Students' material for the course in Artificial Intelligence and Data Engineering at University of Pisa.
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong academic background and a proactive approach to learning new technologies. The diversity of projects, ranging from IoT to networking and distributed systems, indicates a broad technical curiosity. The focus on AI/ML and data-related projects aligns well with a Data Scientist role, suggesting a good cultural fit for a data-driven environment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions are concise, but there is no information on teamwork, problem-solving approaches, or communication style in a professional setting.