Why Politecnico di Milano
After graduating cum Laude from Sapienza, I chose Polimi for its strong focus on systems engineering and its proximity to the European AI research ecosystem. The MSc program offered exactly what I needed: depth in machine learning theory combined with hands-on high-performance computing.
Focus Areas
My coursework and research concentrate on:
- Deep Learning & Representation Learning: advanced architectures, training dynamics, and generalization theory
- Computer Vision & 3D Reconstruction: building on my bachelor's thesis work with Gaussian Splatting
- High-Performance Computing: GPU acceleration, distributed training, kernel optimization (Triton, CUDA)
- Systems Design: scalable architectures for ML inference and training pipelines
Thesis
My thesis work is within the EU Perivallon Horizon Europe project, focusing on deep learning for satellite imagery analysis. The research involves developing efficient neural architectures for environmental monitoring from remote sensing data.
Achievements During the Program
- Merit-Based Scholarship awarded for outstanding academic performance (GPA and credits)
- 3rd Place in the Artificial Neural Networks Challenge (AIRLab), ranked 3rd out of 193 teams with a custom Vision Transformer ensemble for medical image classification
- Active participant in the AI research community at Polimi's AIRLab
What I'm Building
The courses gave me the theoretical foundation; the projects are where it comes together. Flash-Reasoning, Flash-SAE, and the GPU performance research all emerged directly from concepts learned in the HPC and deep learning courses, applied to real problems in AI infrastructure.