Ruisheng Su is an assistant professor in the Department of Biomedical Engineering at Eindhoven University of Technology, where he is part of the Medical Image Analysis group (IMAG/e). His research concerns deep learning for neurovascular image/video analysis, including image registration, segmentation, detection, and quantification, as well as their applications in neurovascular diseases (e.g., stroke, stroke mimics, aneurysms) and image-guided interventions. His research aims to advance AI as a reliable partner to medical professionals. With this in mind, he focuses on developing and validating trustworthy, interpretable AI-based methodologies and practical applications in neurovascular image analysis and image-guided interventions. He has been the organizing chair of the SWITCH stroke workshop since MICCAI 2023. He maintains active collaborations with both academic institutes, such as Erasmus MC and UMC Utrecht, and industrial partners such as Philips. 

Ruisheng Su joined the IMAG/e group of the Eindhoven University of Technology (TU/e) as an assistant professor in April 2024. He obtained his PhD (cum laude) at the Biomedical Imaging Group Rotterdam (BIGR) at Erasmus MC, where his research focused on developing innovative AI-based techniques for medical image/video analysis, particularly in clinical applications of minimally invasive endovascular interventions for acute ischemic stroke. Before 2020, he accumulated five years of industrial experience as a software engineer at ASML, and a research scientist at Philips Research. He earned his master’s degree in electrical engineering from the Technical University of Munich (TUM, Germany) in 2014, specializing in signal and image processing. The research for his master’s thesis was carried out at Philips Research (The Netherlands) on video-based patient activity monitoring. In 2022, Ruisheng received a Van Leersum personal grant from the Royal Netherlands Academy of Arts and Sciences (KNAW) to spend 4 months as a visiting researcher at the Martinos Center for Biomedical Imaging at Harvard Medical School (Boston, USA). In 2023, he was awarded the German DAAD AiNet fellowship to visit and collaborate with several leading research groups in Berlin and Munich.