CV
Machine Learning Engineer with a solid academic background and hands-on experience in deep learning, time series anomaly detection, and computer vision. Skilled in Python, PyTorch, and key ML frameworks, with a strong focus on applying data-driven solutions to real-world challenges. Experienced in developing end-to-end pipelines for data analysis, model training, and deployment. Passionate about AI applications in industrial and automotive contexts, with a proactive and research-oriented mindset.
Education
- M.S. in Computer Science — Data Management and Analysis, Ca’ Foscari University of Venice, 2024
- Thesis: Active Learning with Dynamical System
- Relevant courses: Artificial Intelligence, 3D Geometric Computer Vision, Statistical Inference
- B.S. in Computer Science — Data Science, Ca’ Foscari University of Venice, 2021
- Thesis: Dash AutoML Benchmark
- Relevant courses: Algorithms and Data Structures, Databases, Data Analysis, Predictive Analytics
Work experience
- Oct 2024 – Jun 2026: Machine Learning Engineer & Researcher, NAIS Engineering Srl — Bologna, Italy
- Data Analysis: developed multiple web applications to better visualize project results and analyses using the Dash Plotly framework.
- Data Engineering: built multiple aggregated datasets at different depth levels on Formula 1 engines, with a single record sampled every 0.005 seconds (5 Hz).
- Neural Network Modelling: developed and evaluated different anomaly detection models on time series datasets.
- Nov 2023 – Jan 2024: Probability and Statistics Tutor, Ca’ Foscari University of Venice — Venice, Italy
- Delivered 10 classes on key topics in probability and statistics, helping students succeed in their exam.
- Assisted 30 students in solving statistical problems both manually and using R.
- Provided one-on-one support to clarify statistical concepts and methodologies.
Skills
- Core languages: Python, C++, SQL, R
- Frameworks & libraries: PyTorch, OpenCV, Scikit-learn, Hugging Face, Pandas, NumPy, Matplotlib, Seaborn, Dash Plotly, Flask
- Technologies: Deep Learning, Machine Learning, Computer Vision, Git, Linux, Docker
- Languages: Italian (native), English (B2+, upper-intermediate)
Projects
- Active Learning with Dynamical System — a novel Active Learning methodology that uses a Graph Transduction Game to select the most difficult samples over a subset to add to the labeled set.
- Silhouette-Based Space Carving — reconstructed the 3D shape of an object captured from multiple angles during rotation, progressively removing empty space based on object silhouettes.
- Video Classification with Convolutional Neural Networks — a benchmark over a set of different CNNs on the 1M Sports YouTube Video dataset, inspired by the original paper.
- Dash AutoML Benchmark — a web application to test and evaluate current SOTA AutoML algorithms over a set of Kaggle and OpenML datasets, for both regression and classification tasks.
See all of my work in the portfolio.