• 2513
    Citations - based on content available in repository [source: Scopus]
20132024

Content available in repository

Personal profile

Quote

"I aim at achieving better, faster, and more widely accessible imaging through the design of intelligent systems that efficiently learn how to optimally and autonomously sense, process, and interpret real-world signals."

Research profile

Ruud van Sloun is an Associate Professor in the Signal Processing Systems group of the Electrical Engineering department at Eindhoven University of Technology (TU/e). He works on advanced sensing and signal processing algorithms, with a special focus on deep learning methods for image formation and reconstruction problems. He has a background in probabilistic signal processing for ultrasound imaging exploiting signal structure and models to derive optimal estimators. He develops powerful signal processing and statistical inference solutions that efficiently leverage data / deep learning and model-based signal structure, predominantly applied to imaging problems. Applications span from ultrasound imaging and MRI to automotive radar.

Van Sloun received an ERC starting grant, an NWO VIDI grant, an NWO RUBICON grant, and a Google Faculty Research award. He also received the 2022 TU/e Young Researcher award. 

Academic background

Ruud van Sloun studied Electrical Engineering at Eindhoven University of Technology (TU/e) where he received the MSc and PhD degrees (both cum laude) in 2014 and 2018, respectively. In January 2018, he joined TU/e as an Assistant Professor. Since then, he has been working on deep learning and signal processing for diagnostic (imaging) applications, spending a significant amount of time at foreign research institutes and in industrial labs. 

Affiliated with

Affiliated with

 

 

Partners in (semi-)industry

  • Philips Research
  • Onera

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being

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