Assistant Professor

Ruud van Sloun

I work in artificial intelligence from sensor to interpretation – achieving better, faster and widely-accessible medical diagnostics through sensing systems that efficiently learn how to optimally sense, process, and interpret real-world signals.

Flux 7.078
Group / Department
Signal Processing Systems
Electrical Engineering
Biomedical Diagnostics
Electrical Engineering

Research Profile

Ruud van Sloun is an Assistant Professor in the Signal Processing Systems group of the Electrical Engineering department at Eindhoven University of Technology (TU/e). He works on advanced and intelligent sensing and signal processing algorithms, with a special focus on artificial intelligence in diagnostic ultrasound imaging.
He has a background in probabilistic signal processing for ultrasound-based cancer localization and exploiting signal structure and models to derive optimal estimators. After his PhD, this background has become intertwined with artificial intelligence (AI) and deep learning, to develop powerful signal processing solutions that efficiently leverage data and model-based signal structure. Applications span from AI-driven ultrasound beamforming and image formation to clutter suppression and super-resolution imaging.
Van Sloun has contributed to over 50 scientific publications and 4 patents. In 2019, he received a RUBICON grant on deep learning for next-gen ultrasound from The Netherlands Organization for Scientific Research (NWO).  

Academic Background

Ruud van Sloun studied Electrical Engineering at Eindhoven University of Technology (TU/e) where he received cum laude MSc and PhD degrees in 2014 and 2018, respectively. In January 2018, he joined TU/e as an Assistant Professor. Since then, he has been working on artificial intelligence and signal processing for diagnostic (imaging) applications, spending a significant amount of time at foreign research institutes. Van Sloun also acts as a consultant for Philips Research, where he works one day per week.

Ancillary Activities

No ancillary activities