I am Associate Professor of statistical processing of biosignals at the Electrical Engineering department of Eindhoven University of Technology (TU/e). I am part of the Biomedical Diagnostics lab (BM/d), within the Signal Processing Systems group. I am a member of the Eindhoven Young Academy of Engineering and the chair of the Benelux chapter of the IEEE Engineering in Medicine and Biology Society (EMBS).

Research

My research focuses on quantitative model-driven analysis of medical images and biosignals, with applications spanning cancer imaging, perioperative care, and hemodynamic monitoring.

Cancer growth and aggressiveness are closely linked to angiogenesis — the formation of a chaotic network of leaky microvessels. A central theme of my work is the development of imaging solutions to characterize angiogenesis non-invasively. To go beyond structural imaging, I combine dynamic ultrasound and MRI with contrast agents — including microbubbles and novel (targeted) nanobubbles — to extract structural, functional, and molecular information on angiogenic vasculature at multiple spatial and temporal scales. By merging pharmacokinetic models with machine learning, I aim to bridge mechanistic insight and data-driven discovery, enabling robust and interpretable imaging biomarkers. Correlation with genomic data further opens avenues to uncover the biological mechanisms underlying the observed imaging features.

In the context of perioperative care and hemodynamic monitoring, I focus on model-based analysis of biosignals for accurate prognosis and timely identification of patients at risk. By combining physics-inspired feature extraction with data-driven classification, I develop effective risk prediction tools to support clinical decision making.

Across all my research, I strive to facilitate the translation of results into clinical practice, driven by real clinical and industrial needs through close collaborations with leading healthcare partners.

Teaching

Since 2018, I am the designer and responsible teacher for the course “Statistical Signal Processing”, which is part of the master curriculum in Electrical Engineering and the master curriculum in Artificial Intelligence & Engineering Systems at TU/e. I am also a guest lecturer for the course “Technology for Care and Cure”.

Background

I obtained my BSc and MSc in Biomedical Engineering from the School of Engineering of the University of Pisa (Italy), both summa cum laude. My master graduation project was carried out at Philips Research (Eindhoven, the Netherlands). In 2015, I obtained a professional doctorate in engineering (PDEng) in Healthcare system design from the Stan Ackermans Institute at the Eindhoven University of Technology, which was awarded best project in the designer program in Electrical Engineering. In 2018, I completed my PhD at the Eindhoven University of Technology, with thesis entitled “Pharmacokinetic modeling in cancer: from functional to molecular imaging of angiogenesis”. Both my PDEng and PhD were carried out at the BM/d lab. Here, after a short post-doctoral fellowship, I became assistant professor in statistical processing of biosignals in 2018, with tenure in 2021. I have served as a board member of the Women in Science Eindhoven network (2018-2022), and I am now serving as chair of the IEEE-EMBS Benelux chapter and as a member of Eindhoven Young Academy of Engineering.