About me

I am a postdoctoral researcher in the Department of Information Engineering (DEI) at the University of Padua, working within the Artificial Intelligence, Machine Learning, and Control (AMCO) group, advised by Prof. Gian Antonio Susto.

My current research focuses on creating fairer and more diverse machine learning systems while safeguarding individual privacy. I work on methods to evaluate how models perform across demographic groups, measure the representativeness of datasets, and tackle fairness challenges where sensitive information (e.g., race, disability) is often unavailable. By leveraging quantification learning—a statistical approach to estimate proportions in data—I aim to design systems that promote equity and inclusivity in machine learning models without compromising privacy.

Previously, I completed my PhD at the University of Glasgow in the School of Computing Science, where I was part of the Glasgow Information Retrieval Group. I was supervised by Dr. Richard McCreadie and Dr. Jeff Dalton. My doctoral research focused on transfer learning, specifically on predicting when and why knowledge transfer between tasks leads to improved performance. Using statistical methods and ranking algorithms, I developed a framework to help practitioners identify productive task pairings in sequential transfer learning. This work provided insights into optimising resource usage in training effective models, reducing financial, environmental, and time-based costs while achieving better model performance.