Welcome!
I recently earned my PhD in Computer Engineering from Polytechnic of Turin (Italy) 🎉!
Over the past four years, I've been part of the ELLIS VANDAL lab, working under the guidance of Prof. Barbara Caputo and Dr. Marco Ciccone. I spent part of my last year in the STAIR (Stanford Trustworthy AI Research) group at Stanford University, advised by Prof. Sanmi Koyejo.
My work focuses on trustworthy machine learning, with a specific focus on Federated Learning (FL) and fairness, studied through the lens of the loss landscape and applied to computer vision tasks.
I received both my Master's and Bachelor's degree in Computer Engineering from Politecnico di Torino with top honors, completing them in 2020 and 2018 respectively. My Master’s thesis, Towards Real-World Federated Learning, marked my first deep dive into FL.
I'm also a proud member and former President of the Mu Nu Chapter of IEEE Eta Kappa Nu, the honor society of IEEE.
Research interesets
Artificial Intelligence (AI) has the potential to revolutionize numerous domains, but concerns regarding fairness and responsibility have emerged as critical considerations. Developing AI algorithms that are fair, accountable, and socially responsible is imperative for mitigating biases and ensuring equitable outcomes. In my current research, I focus on leveraging Federated Learning (FL) as a powerful paradigm to reach such goals. FL aims to learn a global model from disparate users' data, while respecting the privacy regulations in force. Looking at realistic scenarios, my main efforts aim to
- Learn models providing fair predictions to the entire data distribution, e.g., not biased towards some less represented demographic groups.
- Broaden the horizons of applicability of FL to real-life vision tasks, such as autonomous driving, or image geo-localization, while introducing realistic constraints, e.g., the absence of labeled data.
For more info and published papers, please visit the Research page.