Dr. Paul Bodesheim – Computer Vision Group Jena

Address:	Friedrich Schiller University Jena Department of Mathematics and Computer Science Institute of Computer Science Computer Vision Group Inselplatz 5 07743 Jena Germany
Room:	3023
E-mail:	paul (dot) bodesheim (at) uni-jena (dot) de
Phone:	+49 (0) 3641 9 46410
Links:	DBLP, GoogleScholar, LinkedIn, ORCID, publons, ResearchGate, SemanticScholar, XING

since Oct 2018		Postdoctoral Researcher and Lecturer Computer Vision Group (Prof. Joachim Denzler), Friedrich Schiller University Jena
since May 2025		Member of ELLIS and ELLIS Unit Jena European Laboratory for Learning and Intelligent Systems
Aug 2022 to Sep 2022		Visiting Academic for two month at the University of Bristol (UK) Research stay in the group of Tilo Burghardt, Department of Computer Science
Jun 2020 to Dec 2022		Team Leader: “Computer Vision and Machine Learning” Computer Vision Group (Prof. Joachim Denzler), Friedrich Schiller University Jena
Jan 2015 to Sep 2018		Research Associate / Postdoc Department Biogeochemical Integration (Prof. Markus Reichstein), Max Planck Institute for Biogeochemistry
Apr 2011 to Dec 2014		Research Associate / PhD Student Computer Vision Group (Prof. Joachim Denzler), Friedrich Schiller University Jena PhD Thesis: “Discovering unknown visual objects with novelty detection techniques”
Oct 2006 to Mar 2011		Studies in Computer Science (Diploma) Friedrich Schiller University Jena Focus: Digital Image Processing / Computer Vision Diploma Thesis: “Object Discovery – Unsupervised Learning of Object Categories”

Best Paper Award at DeLTA 2024
(Kim Bjerge, Paul Bodesheim, Henrik Karstoft: “Few-Shot Learning with Novelty Detection”)
Best Paper Award at Anomaly Detection Workshop of ICML 2016
(Rodner, Barz, Guanche, Flach, Mahecha, Bodesheim, Reichstein, Denzler: “Maximally Divergent Intervals for Anomaly Detection”)
Best Poster Award at FEAST Workshop of ICPR 2014
(Freytag, Rühle, Bodesheim, Rodner, Denzler: “Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods”)
Best Paper Honorable Mention Award at ACCV 2012
(Freytag, Rodner, Bodesheim, Denzler: “Rapid Uncertainty Computation with Gaussian Processes and Histogram Intersection Kernels”)

“Maschinelles Lernen und Datamining” / Machine Learning and Data Mining
- Lecture (4 SWS): 2022/2023, 2021/2022
“Informatik (B.Sc. Werkstoffwissenschaften)” / Computer Science for Materials Scientists
- Lecture (3 SWS): 2024/2025, 2022/2023, 2021/2022, 2020/2021, 2019/2020, 2018/2019
- Exercise (3 SWS): 2018/2019
“Informatik II (B.Sc. Physik)” / Computer Science II for Physicists
- Lecture (2 SWS): 2022/2023, 2021/2022, 2020/2021, 2019/2020, 2018/2019
- Exercise (1 SWS): 2019/2020, 2018/2019

“Mustererkennung” / Pattern Recognition
- Lecture (4 SWS): 2022
“Visuelle Objekterkennung” / Visual Object Recognition
- Lecture (2 SWS): 2024, 2022, 2021, 2020, 2019
“Informatik I (B.Sc. Physik)” / Computer Science I for Physicists
- Lecture (2 SWS): 2025, 2024, 2022, 2021, 2020, 2019
- Exercise (1 SWS): 2020, 2019

Felix Kleinsteuber: “Anomaly Detection in Camera Trap Images”. Bachelor Thesis, 2022. (joint supervision with Daphne Auer)
Philip Fürste: “Detection of Wildlife in Camera Trap Images using Transformers”. Master Thesis, 2022. (joint supervision with Gideon Stein)
Eric Tröbs: “Klassenhierarchien im lebenslangen Lernen” (“Class Hierarchies in Lifelong Learning”). Master Thesis, 2021. (joint supervision with Clemens-Alexander Brust and Dimitri Korsch)
Jan Blunk: “Object Tracking in Wildlife Identification”. Bachelor Thesis, 2021. (joint supervision with Matthias Körschens)
Daphne Auer: “Applying Wavelet Transforms prior to Convolutional Neural Networks for Image Categorization”. Bachelor Thesis, 2020.
Hendrik Happe: “Aktivierungsfunktionen in neuronalen Netzen für die visuelle Objekterkennung” (“Activation Functions in Neural Networks for Visual Object Recognition”). Master Thesis, 2020. (joint supervision with Christian Reimers)
Frank Prüfer: “Detektion von unbekannten Objekten durch Entscheidungswälder und Gauß-Prozesse” (“Detecting Unknown Objects with Decision Forests and Gaussian Processes”). Diploma Thesis, 2013.
Sven Sickert: “Strategien des Aktiven Lernens” (“Strategies in Active Learning”). Diploma Thesis, 2012. (joint supervision with Alexander Freytag)