Junior Research Groups
Michael Hedderich
Dr.
JRG Leader Human-Centered NLP
Human-Centered NLP
leads the MCML Junior Research Group ‘Human-Centered NLP’ at LMU Munich.
His team’s research covers the intersection of machine learning, natural language processing (NLP) and human-computer interaction. Human factors have a crucial interplay with modern AI and NLP development, from the way data is obtained, e.g. in low-resource scenarios, to the need to understand and control models, e.g. through global explainability methods. AI technology also does not exist in a vacuum but must be validated together with the application experts and stakeholders it should serve. The group explores these questions from different perspectives, taking the lense of machine learning, natural language processing and human-computer interaction. By embracing these diverse perspectives, the researcher value how each viewpoint enriches the understanding of the same issues and how different skill sets complement one another.
Almut Sophia Koepke
Dr.
JRG Leader Multi-Modal Learning
Multi-Modal Learning
leads the MCML Junior Research Group ‘Multi-Modal Learning’ at TU Munich.
She and her team conduct research into multi-modal learning from vision, sound, and text. They focus on advancing video understanding, with an emphasis on capturing temporal dynamics and cross-modal relationships. To achieve this, they aim to improve the combination of information from various modalities within learning frameworks. Furthermore, they are exploring how to adapt large pre-trained models for audio-visual understanding tasks. Funded as a BMBF project, the group explores research areas that go beyond our current focus while maintaining a close collaboration with MCML.
Benjamin Lange
Dr.
JRG Leader Ethics of AI
Ethics of AI
leads the MCML Junior Research Group ‘Ethics of Artificial Intelligence’ at LMU Munich.
He and his team conduct research into fundamental and application-related ethical issues relating to AI and ML. They deal with fundamental and practical questions of AI ethics from a philosophical-analytical perspective. By organizing conferences, workshops and panel discussions, the group aims to enter into an interdisciplinary exchange with researchers from philosophy and other disciplines. An important focus here is also communication with the wider public about the moral and social aspects of AI. Another important task of the JRG is the transfer of philosophical-ethical findings and results into practice, for example through collaborations and dialogue with industry and society.
Martin Menten
Dr.
JRG Leader AI for Vision
AI for Vision
leads the MCML Junior Research Group ‘AI for Vision’ at TU Munich.
He and his research group specialize in machine learning for medical imaging. Their research focuses on weakly and self-supervised learning to address data scarcity in healthcare and the integration of multimodal clinical data with medical images. In particular, they are interested in the development and application of machine learning and computer vision algorithms in the field of ophthalmology. Funded by the DFG, the group investigates new research directions that complement and extend MCML’s focus while remaining closely connected to the center.
Tom Sterkenburg
Dr.
Associated JRG Leader Epistemology in ML
Epistemology of ML
leads the Emmy Noether Junior Research Group ‘From Bias to Knowledge: The Epistemology of Machine Learning’ at LMU Munich.
His group’s research is in the epistemological foundations of machine learning. The group uses the mathematical theory of machine learning to study epistemological questions around machine learning and its reliability, with a particular focus on the notion of inductive bias. The group also works on other topics where machine learning and the philosophy of science meet, including explanation and representation. Supported by DFG funding, the group investigates novel research directions that both complement and extend MCML’s scope while strengthening ties to the center.
Xi Wang
Dr.
JRG Leader Egocentric Vision
Egocentric Vision
leads the MCML Junior Research Group ‘Egocentric Vision’ at TU Munich.
Xi Wang and her team conduct cutting-edge research in egocentric vision, focusing on learning from first-person human videos to understand behavior patterns and extract valuable information for potential applications in robotics. Their ongoing projects include 3D reconstruction using Gaussian splitting and multimodal learning with vision-language models. Funded as a BMBF project, the group maintains close ties with MCML and actively seeks collaborations that bridge egocentric vision with other research domains, extending beyond our own focus.
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