Dr. Gabriel Kalweit

Head of Fundamental AI Research

Dr. Gabriel Kalweit holds the position of Head of Fundamental AI Research, focussing on various disciplines within fundamental machine learning. Initially rooted in Reinforcement Learning, his current projects encompass the entire spectrum of learning paradigms within oncology research. His work has garnered recognition by a Best Paper Award Nomination at a leading conference in the field and the selection of one of his supervised projects for presentation at the Lindau Nobel Laureate Meeting in 2023. He mentored over 30 students, one of which received the Thesis Award from the Association of German Engineers (VDI).

His commitment to translating theoretical knowledge into practical applications is demonstrated by the approval of four patents and his ongoing collaboration with the Applied Research group at CRIION.

Working on the Fundamentals of Machine Learning:

Elevating the Foundations of Machine Learning for Complex Problem-Solving:

Decision Complexity: Navigating intricate spaces with scarce data over extended timelines.
Cancer Insights: Innovating tools to decode the complexities of cancer for medical breakthroughs.
Theoretical Strength: Strengthening the core principles of machine learning for robust models.
From Theory to Practice: Transforming advanced theories into practical, impactful solutions.

Publications

Preprints

  • 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.
  • 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.

2023

  • Mehdi Naouar, Gabriel Kalweit, Anusha Klett, Yannick Vogt, Paula Silvestrini, Diana Infante, Roland Mertelsmann, Joschka Boedecker and Maria Kalweit. CellMixer: Annotation-free Semantic Cell Segmentation of Heterogeneous Cell Populations. Oral at NeurIPS 2023 Workshop on Medical Imaging.
  • Yannick Vogt, Mehdi Naouar, Maria Kalweit, Cornelius Miething, Justus Duyster, Roland Mertelsmann, Gabriel Kalweit and Joschka Boedecker. Stable Online and Offline Reinforcement Learning for Antibody CDRH3 Design. NeurIPS 2023 Workshop on Machine Learning in Structural Biology.
  • Gabriel Kalweit, Maria Kalweit, Ignacio Mastroleo, Joschka Bödecker und Roland Mertelsmann. Künstliche Intelligenz in der Krebstherapie. Ordnung der Wissenschaft, 2023.
  • 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.

2022

  • Maria Kalweit, Gabriel Kalweit, Moritz Werling and Joschka Boedecker. Deep Surrogate Q-Learning for Autonomous Driving. ICRA 2022.
  • Jessica Borja-Diaz*, Oier Mees*, Gabriel Kalweit, Lukas Hermann, Joschka Boedecker and Wolfram Burgard. Affordance Learning from Play for Sample-Efficient Policy Learning. ICRA 2022.
  • Gabriel Kalweit, Maria Kalweit, Joschka Boedecker. Robust and Data-efficient Q-learning by Composite Value-estimation. TMLR 2022.
  • Erick Rosete-Beas*, Oier Mees*, Gabriel Kalweit, Joschka Boedecker and Wolfram Burgard. Latent Plans for Task-Agnostic Offline Reinforcement Learning. CoRL 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. Accepted at Digital Biomarkers, 2022

2021

  • Branka Mirchevska, Maria Hügle, Gabriel Kalweit, Moritz Werling, Joschka Boedecker. Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways. 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. IROS 2021.
  • Jessica Borja-Diaz, Oier Mees, Gabriel Kalweit, Lukas Hermann, Joschka Boedecker and Wolfram Burgard. Affordance learning from play for sample-efficient policy learning. NeurIPS 2021 Workshop on Robot Learning.

2020

  • Gabriel Kalweit*, Maria Huegle*, Moritz Werling and Joschka Boedecker. Deep Inverse Q-learning with Constraints. NeurIPS 2020.
  • Maria Huegle, Gabriel Kalweit, Moritz Werling and Joschka Boedecker. Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving. ICRA 2020.
  • Oier Mees*, Markus Merklinger*, Gabriel Kalweit and Wolfram Burgard. Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video. CVPR 2020 Workshop on Learning from Unlabeled Videos.
  • Oier Mees*, Markus Merklinger*, Gabriel Kalweit and Wolfram Burgard. Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video. ICRA 2020 (Nominated for Best Paper Award in Cognitive Robotics).
  • 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).

2019

  • Maria Huegle*, Gabriel Kalweit*, Branka Mirchevska, Moritz Werling and Joschka Boedecker. Dynamic Input for Deep Reinforcement Learning in Autonomous Driving. IROS 2019.
  • 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.
  • Markus Merklinger, Oier Mees, Gabriel Kalweit and Wolfram Burgard. Adversarial Skill Networks: Unsupervised Skill Learning from Video. Extended abstract at RSS 2019 Workshop on Combining Learning and Reasoning – Towards Human-Level Robot Intelligence.
  • 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.

2017

  • Gabriel Kalweit, Joschka Boedecker (2017) Uncertainty-driven Imagination for Continuous Deep Reinforcement Learning. CoRL 2017.