I am an Associate Professor in the Department of Information Engineering, Electronics and Telecommunications (DIET) at Sapienza University of Rome. My research focuses on the development of machine learning methods, with particular emphasis on neural networks, generative models, and representation learning. Rooted in circuit theory and signal processing, my research combines theoretical and algorithmic advances to develop machine learning models capable of analyzing, modeling, and generating complex signals and data across a broad range of applications.

Beyond my research activities, I actively contribute to the international machine learning and signal processing community through scientific leadership, editorial activities, and the organization of major conferences. I currently serve as Chair of the IEEE Machine Learning for Signal Processing Technical Committee and as Chair of the IEEE Task Force on Computational Audio Processing, and I am a member of the Board of Governors of the International Neural Network Society (INNS). I also serve as Area Editor and Associate Editor for leading international journals and have chaired major international conferences, including IEEE MLSP 2023 and IJCNN 2025.

I completed my M.Sc. in Telecommunication Engineering and my Ph.D. in Information and Communication Engineering at Sapienza University of Rome, where I have developed my academic career from doctoral researcher to Associate Professor, under the guidance of Prof. Aurelio Uncini. During my doctoral studies, I was a Visiting Ph.D. Student at Universidad Carlos III de Madrid under the supervision of Prof. Jerónimo Arenas-García. Since my doctoral studies, I have collaborated with academic institutions, research centers, and industrial partners on national and international research projects, contributing to the advancement of machine learning and signal processing

Teaching is an integral part of my academic activity. I currently teach undergraduate, graduate, and doctoral courses covering circuit theory, machine learning, neural networks, and generative models. My teaching reflects the close connection between engineering foundations and modern machine learning, bringing research topics into both graduate and doctoral education.

Recognition from the international research community includes scientific and editorial awards. Among these are the Outstanding Editorial Board Member Award (2025) from IEEE Signal Processing Magazine, the Outstanding Associate Editor Award (2022) from IEEE Transactions on Neural Networks and Learning Systems, the Best Paper Award at IEEE ISCAS 2022 and AISV Conference 2012 and 2013.

Further information about my research, publications, teaching, professional activities, and student supervision is available in the corresponding sections of this website.