Michael Hedderich and his team research the intersection of machine learning, NLP, and human-computer interaction, focusing on human factors, explainability, and validating AI with application experts to ensure impactful, context-aware solutions.
Almut Sophia Koepke and her team research multi-modal learning from vision, sound, and text, focusing on video understanding, temporal dynamics, cross-modal relationships, and adapting large pre-trained models for audio-visual 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 and his team explore ethical issues in AI and ML, combining philosophical analysis with interdisciplinary exchange, public engagement, and practical collaborations to address moral and social implications.
Marin Menten and his team focus on machine learning for medical imaging, specializing in data-scarce healthcare applications and ophthalmology through multimodal integration and advanced algorithms. Supported by DFG funding, the group explores new research directions that complement and expand MCML’s scope while maintaining a strong connection to the center.
Tom Sterkenburg and his groups’s research is in the epistemological foundations of machine learning. 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 and her team research on understanding dynamic activities and behavior captured through egocentric views. 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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2024-10-11 - Last modified: 2024-12-23