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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. Jonas Alle, M.Sc.

Jonas Alle

Tim Kipphan/Uni Bamberg

Teaching and Research Assistant

M.Sc., Doctoral Candidate

Address:      An der Weberei 5, 96047 Bamberg
Room:          WE5/04.090

Email:           jonas.alle(at)uni-bamberg.de

Consultation hour:

is possible by appointment via email

Biography:

Jonas Alle is a dedicated PhD candidate at the Otto-Friedrich University of Bamberg, focusing on the field of Explainable Machine Learning (xAI) since 2024. Jonas holds a master’s degree in Computational Engineering with a focus on Deep Learning and Computational Materials Science, which he earned at the Friedrich-Alexander-University Erlangen-Nuremberg. He also completed a bachelor's degree in Applied Mathematics and Physics at the university of applied sciences Georg-Simon-Ohm in Nuremberg, where he focused on Dynamic Systems and Machine Learning.

His current research centers around the investigation of information flow through deep neural networks, using the perspectives of geometry, probability, or mathematical rigor, to ultimately aim for faithful and robust uncertainty estimations with Bayesian Neural Networks.

Prior to his doctoral studies, Jonas gained significant experience as a Student Research Assistant at the Fraunhofer Institute for Integrated Circuits (IIS), Development Centre for X-Ray Technology. There, he developed deep neural networks for semantic- and instance segmentation tasks for reconstructed 3D images of CT-scans. His bachelor’s thesis focused on plant root segmentation using Convolutional Neural Networks, and the development of an adaptive and efficient inference algorithm. During his master’s thesis, he explored uncertainty estimation techniques for deep learning models and integrated them into a dynamic flood-filling framework for instance segmentation of XXL-CT data.

Jonas has engaged in various extracurricular and professional development activities in 2023. He participated in the AI safety bootcamp "Machine Learning for Good" in Paris and followed the eight-week AI Alignment course by BlueDot Impact. He also attended the Probabilistic Numerics Spring School at Eberhard Karls University in Tübingen.

Outside of academia, Jonas has a passion for music, frequently visiting concerts and discovering new artists across various genres including rock, metal, and folk. He, also enjoys indoor bouldering.

Profiles: LinkedIn, GitHub, ORCID