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Information Systems and Applied Computer Sciences

Chair of Explainable Machine Learning

  1.  University of bamberg
  2. Faculties
  3. Information Systems and Applied Computer Sciences
  4. Subject groups
  5. Applied Computer Science
  6. Chair of Explainable Machine Learning
  7. Team
  8. Prof. Dr. Christian Ledig

Prof. Dr. Christian Ledig

Tim Kipphan/Uni Bamberg

Chair holder

Adress:                   An der Weberei 5, 96047 Bamberg
Room:                     WE5/04.083

phone:                   +49-951-863 2026 (secretary)

E-mail:                    christian.ledig(at)uni-bamberg.de

Consultation hour:
during lecture times: 
Monday 4 p.m. to 5 p.m. in WE5/04.083 (notification via  Email recommended)

outside lecture times: by appointment

Biography

Prof. Dr. Christian Ledig is a full professor at the Otto-Friedrich-University Bamberg. Since April 2022, he has been leading the Chair of Explainable Machine Learning at the Faculty of Information Systems and Applied Computer Science. Prof. Ledig is a member of ELLIS and of the management board of the Bamberg Center for Artificial Intelligence (BaCAI). He leads the xAILab Bamberg that conducts research with focus on explainable and robust machine learning approaches and their translation into practice, particularly in healthcare.

Prof. Ledig earned his doctorate (PhD, 2015) at Imperial College London, UK, in the field of Medical Image Analysis in the BioMedIA group under the supervision of Prof. Daniel Rueckert on the topic of “Robust Multi-Structure Segmentation of Magnetic Resonance Brain Images” [doi]. As part of various research projects, Prof. Ledig has worked at the Boston Children's Hospital at Harvard Medical School in the USA (Computational Radiology Laboratory (CRL)) and at the University of Cambridge in the UK (Department of Medicine, Division of Anaesthesia). 

Before joining the BiomedIA group he obtained a Diploma degree in Applied Mathematics (Dipl. Univ. Technomath., equiv. MSc.) from the Friedrich-Alexander University Erlangen-Nuremberg, Germany and was interning at Siemens Corporate Research, NJ, USA as Research Intern. During this time he wrote his Diploma thesis on the “Efficient Implementation of Nonrigid Registration Methods on commodity Hardware with CUDA” [pdf]. During his studies he was working with Siemens AG (Healthcare and Industry) for several years. Prior to that he has finished a three year apprenticeship as SoftwareEngineer@BellLabs with Lucent Technologies in Nuremberg, Germany. He is an alumni of the Siemens training program Topaz and the Leonardo Kolleg of the University of Erlangen-Nuremberg.

From 2013 to 2015 he was working part-time as a Senior Imaging Scientist for IXICO, plc, commercialising selected algorithms of his research. Before his professorship, Prof. Ledig conducted AI research in the industry, including positions at Twitter (now X) in London (2016-2017) and most recently in the US startup sector (2018-2021) in Manhattan, NY (Sr. Research Scientist at Imagen Technologies) and Boston, MA (Head of AI at VideaHealth). There, Prof. Ledig led research teams and successfully developed FDA-regulated, AI-driven healthcare systems that are now approved and improve patient treatment.

In 2011 he established the ICPC student program for competitive programming at Imperial College London. In 2016, he coached an Imperial College Student team to the ACM ICPC world finals in Phuket, Thailand [article].

He has more than 15 years of research experience in machine learning, computer vision, and image analysis in both industry and academia. His focus is on the development of AI-driven medical applications that enable better healthcare by reducing diagnostic errors and improving access to high-quality medical diagnoses. Throughout his career, Prof. Ledig has published two book chapters and more than 75 peer-reviewed research articles, which have been cited over 32,000 times in total. That places Prof. Ledig among the top 2% of the most-cited scientists worldwide. 

Additionally, he is active as a reviewer for various leading conferences and journals, such as the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), ACM SIGGRAPH, ACM SIGGRAPH ASIA, Medical Image Computing and Computer Assisted Intervention (MICCAI), Medical Image Analysis and IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 

He regularly serves as Area chair (AC) for internationally renowned conferences such as MICCAI 2025, 2026 or IJCAI 2026 and currently serves as Associate Editor for Pattern Recognition (Elsevier).