Dr. Maria Kalweit

Head of Applied AI Research

Dr. Maria Kalweit currently serves as the Head of Applied AI at CRIION while also holding a PostDoctoral position at the Faculty of Engineering at the University of Freiburg. Her work in applied artificial intelligence, particularly in medical contexts, is grounded in a methodical approach to developing machine learning models that maintain functionality under resource constraints and with varied data inputs. The scope of her work encompasses the entire development pipeline, extending from model formulation to web and interface refinement, along with continuous integration and the creation of interpretation tools. Dr. Kalweit has actively contributed to the development of a digital biomarker and advanced her work towards four distinct patents. Her research has earned her a Best Paper Award for her ultra-low powered seizure detection device and the Wolfgang-Gentner-Award for her doctoral thesis. Through her roles at CRIION and the University of Freiburg, she contributes to the progress of applied AI in the medical field, focusing on creating accessible and reliable tools for use in diverse and challenging environments.

Working on challenges of bringing AI to the real world:

  • Efficiency: satisfy runtime, data or memory constraints
  • Interpretability: explanations for the models decisions
  • Accessibility: make AI available for non-experts
  • Trustworthiness: reliable, fair, transparent, and safe for all users
  • Deployment: provide usable interfaces to AI models



  • Mansour Alyahyay, Gabriel Kalweit, Maria Kalweit, Golan Karvat, Julian Ammer, Artur Schneider, Ahmed Adzemovic, Andreas Vlachos, Joschka Boedecker and Ilka Diester. Mechanisms of Premotor-Motor Cortex Interactions during Goal Directed Behavior. 2023.
  • Marieke Wesselkamp, Niklas Moser, Maria Kalweit, Joschka Boedecker, Carsten F. Dormann. Process-guidance improves predictive performance of neural networks for carbon turnover in ecosystems.
  • Paul Schmidt-Barbo, Gabriel Kalweit, Mehdi Naouar, Lisa Paschold, Edith Willscher, Christoph Schultheiss, Bruno Markl, Stefan Dirnhofer, Alexandar Tzankov, Mascha Binder und Maria Kalweit. Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning. 2023.


  • Maria Kalweit, Andrea Burden, Joschka Boedecker, Thomas Hügle and Theresa Burkard. Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs. PLoS Computational Biology, 2023.
  • Gabriel Kalweit, Maria Kalweit, Ignacio Mastroleo, Joschka Bödecker und Roland Mertelsmann. Künstliche Intelligenz in der Krebstherapie. Ordnung der Wissenschaft, 2022.
  • Mehdi Naouar, Gabriel Kalweit, Ignacio Mastroleo, Philipp Poxleitner, Marc Metzger, Joschka Boedecker and Maria Kalweit. Robust Tumor Detection from Coarse Annotations via Multi-Magnification Ensembles. Poster at Digital Oncology, Hannover 2023.


  • Maria Kalweit, Gabriel Kalweit, Moritz Werling and Joschka Boedecker. Deep Surrogate Q-Learning for Autonomous Driving. ICRA 2022.
  • Gabriel Kalweit, Maria Kalweit, Joschka Boedecker. Robust and Data-efficient Q-learning by Composite Value-estimation. TMLR 2022.
  • Thomas Hügle, Leo Caratsch, Matteo Caorsi, Jules Maglione, Alexandre Dumusc, Diana Dan, Marc Blanchard, Gabriel Kalweit and Maria Kalweit. Automated Recognition and Monitoring of Dorsal Finger Folds by a Convolutional Neural Network as a Potential Digital Biomarker for Joint Swelling in Patients with Rheumatoid Arthritis. Digital Biomarkers, 2022.


  • Branka Mirchevska, Maria Hügle, Gabriel Kalweit, Moritz Werling, Joschka Boedecker. Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways. Accepted at ICRA 2021.
  • Maria Hügle, Ulrich A Walker, Axel Finckh, Ruediger Mueller, Gabriel Kalweit, Almut Scherer, Joschka Boedecker, Thomas Hügle. Personalized Prediction of Disease Activity in Patients with Rheumatoid Arthritis Using an Adaptive Deep Neural Network. PLOS ONE.
  • Maria Kalweit, Gabriel Kalweit and Joschka Boedecker. AnyNets: Adaptive Deep Neural Networks for Medical Data with Missing Values. IJCAI 2021 Workshop on Artificial Intelligence for Function, Disability, and Health.
  • Maria Kalweit, Gabriel Kalweit, Moritz Werling and Joschka Boedecker. Deep Surrogate Q-Learning for Autonomous Driving. IJCAI 2021 Workshop on Artificial Intelligence for Autonomous Driving.
  • Gabriel Kalweit, Maria Kalweit, Mansour Alyahyay, Zoe Jaeckel, Florian Steenbergen, Stefanie Hardung, Ilka Diester and Joschka Boedecker. NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding. ICML 2021 Workshop on Computational Biology.
  • Gabriel Kalweit, Maria Huegle, Moritz Werling and Joschka Boedecker. Q-learning with Long-term Action-space Shaping to Model Complex Behavior for Autonomous Lane Changes. Accepted at IROS 2021.
  • Hügle T, Kalweit M. Artificial intelligence-supported treatment in rheumatology: Principles, current situation and perspectives. Zeitschrift fur Rheumatologie. 2021


  • Gabriel Kalweit*, Maria Huegle*, Moritz Werling and Joschka Boedecker. Deep Inverse Q-learning with Constraints (Accepted at NeurIPS 2020).
  • Maria Huegle, Gabriel Kalweit, Moritz Werling and Joschka Boedecker. Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving. In Proc. of the IEEE International Conference on Robotics and Automation (ICRA, 2020).
  • Maria Hügle, Gabriel Kalweit, Thomas Hügle and Joschka Boedecker. A Dynamic Deep Neural Network For Multimodal Clinical Data Analysis. AAAI 2020 Workshop on Health Intelligence. Explainable AI in Healthcare and Medicine. Studies in Computational Intelligence, Springer (2020).
  • Maria Hügle, Patrick Omoumi, Jaap van Laar, Joschka Boedecker and Thomas Hügle. Applied Machine Learning and Artificial Intelligence in Rheumatology. Rheumatology Advances in Practice (2020).


  • Maria Huegle*, Gabriel Kalweit*, Branka Mirchevska, Moritz Werling and Joschka Boedecker. Dynamic Input for Deep Reinforcement Learning in Autonomous Driving (IROS 2019).
  • Maria Hügle, Joschka Boedecker and Thomas Hügle. Clinical Event-based Adaptive Deep Learning for the Prediction of Disease Progression in Rheumatoid Arthritis (Abstract and poster at Frontier of AI-Assisted Care Scientific Symposium, Stanford University, 2019).
  • Maria Hügle, Gabriel Kalweit, Moritz Werling, and Joschka Boedecker. Learning Dynamic Representations for Deep Reinforcement Learning in Autonomous Driving (Extended abstract at RSS 2019 Workshop on Scene and Situation Understanding for Autonomous Driving).
  • Gabriel Kalweit, Maria Huegle and Joschka Boedecker. Composite Q-Learning. Extended abstract at RSS 2019 Workshop on Combining Learning and Reasoning – Towards Human-Level Robot Intelligence.


  • Maria Hügle, Simon Heller, Manuel Watter, Manuel Blum, Farrokh Manzouri, Matthias Dümpelmann, Andreas Schulze-Bonhage, Peter Woias, Joschka Boedecker. Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller (IJCNN 2018, won the “Best Paper Award”).
  • Simon Heller, Maria Hügle, Iman Nematollahi, Farrokh Manzouri, Matthias Dümpelmann, Andreas Schulze-Bonhage, Joschka Boedecker, Peter Woias. Hardware Implementation of a Performance and Energy-Optimized Convolutional Neural Network for Seizure Detection (EMBC 2018).


  • Molteni P, Hügle T, Hügle M, Nüesch C, Mündermann A. Reduction in ulnar pressure distribution when walking with forearm crutches with a novel cuff design: Cross-sectional intervention study on the biomechanical efficacy of an ulnar recess (2016).