Trust in AI: MCML Delegation Visit
The Munich Center for Machine Learning (MCML) is a joint research initiative of Ludwig-Maximilians-Universität München (LMU) and Technische Universität München (TUM). As part of the German and Bavarian governments’ AI strategy, it is one of six nationally and permanently funded AI Competence Centers in Germany.
The three research areas of MCML include:
- Machine Learning – including statistical foundations and explainability, mathematical foundations, and computational models.
- Perception, Vision, and Natural Language Processing – including computer vision, natural language processing, and multimodal perception.
- Domain-Specific Machine Learning – including applications in biology, medicine, physics, geosciences, computational social sciences, and human-centered artificial intelligence.
The MCML delegation visit to the United States, supported by the DWIH New York, aims to strengthen transatlantic ties in the field of artificial intelligence and machine learning. Central to this visit is the goal of building collaborative networks by expanding existing partnerships and establishing new research collaborations with leading institutions such as Harvard, MIT, NYU, Cornell Tech, and Northeastern. Through these engagements, the delegation seeks to foster knowledge exchange and facilitate dialogue on cutting-edge research, with a particular emphasis on promoting societal trust and exploring the broader benefits of AI.
In addition, the visit is intended to lay the groundwork for future research exchanges, contributing to MCML’s vision of a global network of research excellence spanning the US, Europe, and Asia. Key focus areas of the delegation include generative AI and computer vision, examining how advanced generative techniques can revolutionize sectors such as the creative industries and healthcare, while addressing challenges related to fairness, transparency, and social integration. Another priority is medical AI and machine learning for healthcare, with discussions centered on the role of AI in medical imaging, diagnostics, and personalized medicine, as well as the ethical and practical implications of its responsible integration into healthcare systems. Further thematic interests include social data science and the foundational principles of machine learning.