Paul Bodesheim

Dr. Paul Bodesheim serves as a Postdoctoral Researcher and Lecturer in the Computer Vision Group at Friedrich Schiller University Jena's Department of Mathematics and Computer Science. He is also a member of ELLIS (European Laboratory for Learning and Intelligent Systems) and ELLIS Unit Jena. His academic journey began with Computer Science studies at Friedrich Schiller University Jena, focusing on Digital Image Processing and Computer Vision, followed by PhD research on "Discovering unknown visual objects with novelty detection techniques."

Dr. Bodesheim's research interests center on bridging computer vision with biodiversity applications. His work spans visual object recognition, fine-grained recognition techniques specifically applied to biodiversity research, novelty detection and open set recognition, and active learning approaches for lifelong learning systems. He has developed approaches that address challenges of learning from small and imbalanced datasets, which are particularly valuable in ecological monitoring where data collection can be challenging.

His publication record reveals a strong trend connecting computer vision methodologies with biodiversity monitoring applications. A significant portion of his recent work focuses on automated visual monitoring systems for insects and mammals, particularly using camera traps for biodiversity assessment. His research demonstrates how techniques like deep learning, few-shot learning, and uncertainty quantification can be effectively applied to ecological challenges such as species identification and population monitoring.

• Best Paper Award at DeLTA 2024 (Few-Shot Learning with Novelty Detection)
• Best Paper Award at Anomaly Detection Workshop of ICML 2016
• Best Poster Award at FEAST Workshop of ICPR 2014
• Best Paper Honorable Mention Award at ACCV 2012

Dr. Bodesheim has supervised numerous theses across bachelor's, master's, and diploma levels, focusing on applications of computer vision to biodiversity monitoring. His research is supported by various projects including LEPMON (Monitoring Biodiversity of Moths), InsectAI (EU COST Action Network), and previously BACI and AMMOD projects. He serves as a reviewer for prestigious journals and conferences including IEEE TPAMI, JMLR, and CVPR workshops.

He leads the Computer Vision and Machine Learning team within the Computer Vision Group and contributes to the development of the AMMOD (Automated Multisensor station for Monitoring of species Diversity) system, which integrates multiple sensor technologies for comprehensive biodiversity monitoring. His work demonstrates a strong commitment to developing practical computer vision solutions for pressing ecological challenges.