Meet the Team

Lab Leadership

Daniel Truhn
Daniel is a physicist, imaging scientist, and clinical radiologist with a dedicated focus on machine learning and magnetic resonance imaging. After studying physics at RWTH Aachen University and Imperial College in London, he continued to satisfy his thirst for knowledge by studying medicine at RWTH Aachen University. In 2013, he completed his MD thesis on the compatibility of positron emission tomography and magnetic resonance imaging and joined the Department of Diagnostic and Interventional Radiology (University Hospital Aachen) to become a board-certified clinical radiologist in 2019. Besides his clinical work, he pursued his research interests in machine learning as a fellow at the Institute of Imaging and Computer Vision (RWTH Aachen University) for two years before returning to the clinic where he currently leads the interdisciplinary research group “AI in Medical Imaging”. His research focuses on bringing machine learning-methods into clinical practice and on bridging the gaps between research possibilities and clinical applicability.His recent publications are listed on Google Scholar and Pubmed.

Senior Scientist

Sven Nebelung
Sven is a clinical radiologist and imaging scientist focusing on clinically motivated imaging research that aims to refine image acquisition and post-processing methodologies in close collaboration with clinicians, clinical scientists, engineers, physicists, and imaging scientists. After studying medicine at RWTH Aachen University, he completed his MD thesis on cartilage tissue engineering. He joined the Department of Orthopedics (University Hospital Aachen) to receive orthopedic training during the surgical common trunk. After undertaking research at the Institute of Anatomy (RWTH Aachen University) in 2015, he entered Radiology specialist training at the Department of Diagnostic and Interventional Radiology (University Hospital Aachen). After completing a research stay at the Department of Diagnostic and Interventional Radiology (University Hospital Düsseldorf) from 2019 to 2021, he moved back to Aachen to lead the group. In 2022, he was board-certified as a radiologist and has been working as an attending physician I the Department of Diagnostic and Interventional Radiology (University Hospital Aachen) ever since, focusing his clinical work on MRI and musculoskeletal pathologies. His research is generously funded by the German Research Association (DFG) and funds from RWTH Aachen University. His recent publications are listed on Google Scholar and Pubmed. He also regularly reviews manuscripts for medical, technical, and interdisciplinary scientific journals.

Research Coordinator

Vera Winter
Vera joined the team in January 2024 as research coordinator. She has a vast experience in the field of research funding and project management and is passionate about supporting researchers so they can focus on the science. Before joining our team she was in charge of EU funded research projects, in particular European Research Council (ERC), at RWTH Aachen University and worked in project and stakeholder management at the European Spallation Source in Lund, Sweden. Vera has studied European Studies, International Relations and Research Management in Maastricht, Braga, Malmö and Speyer.

PhD Students

Debora Jutz
Debora is a medical computer scientist focusing on machine learning classifying breast magnetic resonance images. She studied medical computer science at University Heidelberg and Heilbronn University for the bachelors degree. She continued her academic journey in Tübingen where she worked as a research assistant in 2022, focusing on privacy-preserving machine learning techniques. This role allowed her to deepen her expertise in safeguarding sensitive data during model training and inference. Debora completed her M.Sc. in medical computer science from the University of Tübingen in 2023, specializing in security. Her master's thesis focused on implementing inference on a privacy-preserving Convolutional Neural Network (CNN) using secure three-party computation.

Mahta Khoobi
Mahta is a doctoral student at Uniklinik RWTH Aachen, delving into Machine Learning and Musculoskeletal Imaging since January 2024. She's on a mission to create user-friendly interfaces for medical experts, integrating AI models seamlessly while keeping them explainable in medical contexts. Leveraging open source platforms, she's optimizing user interaction for efficient AI integration into clinical practice. Before her doctoral studies, Mahta earned an M.Sc. in Software Systems Engineering from RWTH Aachen and a B.Sc. in Computer Engineering from Tehran, Iran. With industrial experience in backend and frontend development, she's worked on various projects, including financial data services and banking web apps. Mahta's research interests range from software engineering for machine learning to user interface design in medical imaging platforms and MLOps. Outside work, she enjoys fitness, painting, travel, and continuous learning.

Hanna Kreutzer
Hanna completed her Bachelor’s in Biotechnology at RWTH and wrote her thesis on pharmacokinetical modeling. She then decided to combine her interest in medicine and technology by switching to Biomedical Engineering for her master’s. During a research internship on MRI image reconstruction at the Montreal Neurological Institute, she discovered her love for medical imaging and everything data-related. Therefore, she further explored the field afterwards by writing her master’s thesis at Forschungszentrum Jülich on the topic of fMRI and qMRI image analysis. Now, she’s excited to keep learning about AI and how it can be leveraged in the clinical context.

Patrick Wienholt
Patrick is a PhD (Dr. rer. medic.) candidate and researcher. He has studied computer science and received his bachelor's and master's degrees from RWTH Aachen University. During his studies, he focused on machine learning with a special emphasis on computer vision. Since the beginning he took lectures with a focus on medicine and medical engineering. In his research, he uses his expertise to push medical deep learning research beyond the current state of the art.

Roman Vuskov
Roman is a passionate PhD Student (Dr. rer. medic) blending his academic foundation in computer science with a keen interest in medical AI. Having completed both his master's and bachelor's degrees at RWTHS Aachen, he has experience in machine learning, data science, and high-performance computing. His academic journey has been driven by a fascination with expanding the problem-solving capabilities of artificial computational systems and extracting valuable insights from challenging datasets. Presently, he collaborates closely with medical experts, leveraging established deep-learning techniques to unlock new clinical insights while conducting research into novel deep-learning methods for the medical domain.

Simon Westfechtel
Simon studied Computational Social Systems at RWTH Aachen for his Master's degree and joined the group in 2021 as a student researcher. After completing his studies, he became a full-time PhD student and research fellow in 2023. His work is focused on developing novel methods for anatomical landmark detection in 3D magnetic resonance imaging and building AI-aided diagnostic tools for clinical practice.

Postdoctoral Researchers

Alexander Hermans
Alexander Hermans holds bachelor, master and PhD degrees in computer science from RWTH Aachen University. During his PhD at the Computer Vision group he worked on deep learning based approaches for computer vision with a strong focus on real-world robotics applications. He currently serves as a postdoctoral researcher in the Computer Vision group at RWTH Aachen University, while also contributing to the Machine Learning and Musculoskeletal Imaging group at the university hospital. His work focuses on the intersection of computer vision and practical applications in robotics as well as medical imaging.

Dennis Eschweiler
Dennis Eschweiler is a research scientist at the Machine Learning and Musculoskeletal Imaging group at the RWTH University Hospital. He received bachelor's and master's degrees in electrical engineering from RWTH Aachen University in 2015 and 2018, respectively. During his PhD at the Institute of Imaging and Computer Vision at RWTH Aachen University, he worked on deep learning-based approaches for segmentation and generative models for 3D biomedical image data. He joined the group in 2024 and is involved in various projects with the primary goal of bringing deep learning-based approaches into clinical practice.

Firas Khader
Firas is a Postdoc with a focus on applying machine learning techniques to medical data. Prior to joining the group in 2020 he received his bachelor’s degree in Electrical Engineering at Hamburg University of Technology and his master’s degree at RWTH Aachen University. In his master thesis he conducted research on deep learning-based behaviour analysis that was followed up by an internship as a machine learning-engineer in the industry where he incorporated MRI knee segmentation algorithms into the workflow of clinical radiologists. During his PhD, his main focus was on analysing multimodal medical data (radiology & pathology) using deep learning, as well as working on generative models for medical imaging. His current focus is on exploring the capabilities of large-language models on medical data.

Gustav Müller-Franzes
Gustav studied electrical engineering at RWTH Aachen University. He developed deep learning methods for the classification of breast cancer when pursuing his bachelor thesis at the Institute of Imaging and Computer Vision (RWTH Aachen University). Following that, he investigated the effects of inter-rater segmentation variance on radiomics features during his master thesis. He joined the team in 2020 and is now a PhD candidate involved in several projects with the primary goal to establish and validate deep learning methods in clinical practice. Currently, he is working on machine learning-based optimization strategies in breast cancer screening, joint MRI post processing, and other aspects of medicine.

Soroosh Tayebi Arasteh
Soroosh is an AI scientist and electrical & computer engineer. He completed his M.Sc. in signal processing and communications engineering at the University of Erlangen-Nuremberg (FAU). Keen to delve deeper into research and innovation, Soroosh embarked on his master's thesis at Harvard Medical School. He joined the group in 02/2022 for a PhD in AI in Medical Image Processing from RWTH Aachen University. Currently, he is a postdoctoral researcher at the group, working on multiple projects such as privacy-preserving medical deep learning, generative AI, and multimodal AI. For a more detailed insight, visit his personal website Here.

Tianyu Han
Dr. Tianyu Han is a postdoc researcher at the University Hospital RWTH Aachen's Department of Diagnostic and Interventional Radiology. With a PhD in Physics from RWTH Aachen University, his doctoral study explored generative modeling for medical image analysis, resulting in multiple high-impact publications in Nature Machine Intelligence, Nature Communications, Radiology, and Science Advances. Since completing his PhD, Dr. Han has extended his research to developing and deploying advanced large language models (LLMs) for medical applications. His notable contributions include the creation of MedAlpaca, an open-source medical LLM, and the compilation of Medical Meadow, a substantial medical dataset designed for LLM training. His research has demonstrated the practical utility of multimodal LLMs in clinical settings, contributing to their adoption for general medical image interpretation. Dr. Han’s work has also led to significant advancements in using adversarially trained models for pathology detection and the anonymous sharing of medical data through generative models. His innovative methodologies have not only enhanced diagnostic accuracies but also facilitated the understanding of radiological confounders on an individual level.