AI Systems Graduate • Computer Vision • NLP • Deep Learning
Passionate about exploring all aspects of Artificial Intelligence - from computer vision
to natural language processing with deep learning and classical machine learning.
Eager to apply my knowledge in real-world scenarios and continue growing in this exciting field.
Hire me!
Eurecat Technology Center | Barcelona, Spain | April 2024 - June 2024
Explored log-probability-based uncertainty of Large Language Models (LLMs) applied to planning and choice-selection. Developed approaches using GPT 3.5, GPT 4o, and Llama 3.1, creating CSV-based preference collections with RAG to reduce uncertainty in recurrent tasks.
University of Trento | Trento, Italy | January 2021 - May 2021
Rebuilt a modular Android 11 app for tracking healthy habits using Kotlin and Firebase. Implemented Google login, NoSQL storage, and designed 23+ UI screens with future extensibility features.
University of Trento | Trento, Italy | September 2021 - March 2025
Thesis: "Exploring the Use of LLMs for Agent Planning: Strengths and Weaknesses" - Research on Large Language Models applied to automated planning and decision-making scenarios.
Relevant Courses: Machine Learning, Deep Learning, Natural Language Understanding, Automated Planning, Law & Ethics in AI, Fundamentals of AI (Reasoning and Planning), Signal Processing, AI for Finance, High-Performance Computing.
University of Trento | Trento, Italy | September 2017 - June 2021
Thesis: "Healthy Plus - Redesign and evolution of an Android application for monitoring healthy lifestyles" - Complete redesign and modular development of a health tracking mobile application.
Relevant Courses: Algorithms and Data Structures, Advanced Algorithms, Database, Software Engineering, Networks, Geometry and Linear Algebra, Calculus 1, Computer Architectures, Operating Systems, Mobile Programming (Android).
Designed a multi-stage deep learning model to classify Lung Ultrasound images based on a 0-to-3 illness score. Developed 3 components: multi-class frame classifier, uncertainty detection model, and similarity module using 47k frames from 14 real patients.
Implemented 4 Deep Learning models for simultaneous Intent Detection and Slot Filling tasks. Achieved 13% improvement on SNIPS dataset in slot filling task. Fine-tuned BERT and ERNIE, built Bidirectional LSTM and Encoder-Decoder models from scratch.
Built and evaluated a deep learning model for Unsupervised Domain Adaptation using ResNet34 with custom adaptation layer. Achieved +11.53% improvement over baseline non-adapted model on Adaptiope dataset using 3rd and 4th-order statistics.
Designed autonomous agent solution for delivery problems using Belief-Desire-Intent framework. Developed and tested both single-agent and collaborative multi-agent environments across 7 challenges with 2 different approaches.
Provided a solution to Closest Pair of Points problem in N dimensions with parallel implementation. Implemented both Bruteforce and Divide et Impera solutions using Message Passing Interface (MPI) in C. Tested with up to 250M points using 1 to 80 CPU cores in 4 different configurations on the University's cluster.