Fresh impetus for data science
TUM opens Munich Data Science Institute
Developments in machine learning, AI, natural language processing and computer-based imaging have started to fundamentally transform society, the economy and even gain of knowledge of science. With the goal of advancing the foundations of modern data sciences, machine learning and AI and applying these insights in high-potential applications, TUM has founded the Munich Data Science Institute (MDSI). As an integrative research institute, MDSI will pool the expertise of more than 60 professors across interdisciplinary boundaries.
The official opening of the institute took place on Thursday in the Galileo Building at the TUM Research Campus in Garching.
En route to a global brand for collaborative data science
In his keynote address at the opening of the MDSI, Bavaria’s science minister Markus Blume said: “Data are the treasure of our time. The Munich Data Science Institute is our key to the treasure chest and will open the door to innovation. In the MDSI, TUM is bringing together what must come together in the world of data science: business and science, fundamental research and applications. Because only through collaboration and a strong interdisciplinary network we can play a role in shaping the big transformation of the digital age.”
President Prof. Thomas F. Hofmann said: “To effectively leverage the potential of the age of machine learning and AI, we need to see modern data science as a team sport. With the MDSI, we are delivering fresh impetus to data-based technology developments and integrating them into real-world applications. Machine learning and AI harbor enormous potential. From life sciences and medicine, material and design sciences to quantum science, astrophysics and climate science – as well as the dynamics of societal, political and economic systems – the MDSI will support pioneering data science experts in reshaping the boundaries of what is now feasible.”
Stephan Günnemann, Executive Director of the MDSI and Professor of Data Analytics and Machine Learning, said: “At the MDSI we want to study the foundations of modern data science. This relates to the areas of mathematics and informatics that deal with machine learning. But we also want to apply what we learn in specialized areas such as the development of new materials or in personalized medicine.”
Another goal of the institute is to communicate research results to the world of business and society at large to bring about a transfer of AI-based solutions to industry partners and data-related startups. The MDSI will also support researchers in meeting the growing need for data-related tasks in their research and will serve as a network for interdisciplinary contacts between AI experts.
Convergence of competencies under the MDSI roof
Purely quantitative growth by adding new disconnected activities, one after another, in the fields of data science and programs will not have the necessary impact to reach global player status. “That is why we are bundling our strategic data-supported activities under the organizational roof of the MDSI. This will minimize redundancies across TUM and promote synergy effects between disciplines,” says President Prof. Thomas F. Hofmann.
The following initiatives and facilities will be placed under the MDSI:
- An example of a domain-specific MDSI activity is the TUM Georg Nemetschek Institute – Artificial Intelligence for the Built World. It was established under the roof of the MDSI with the support of a 50 million euro donation from the Nemetschek Innovation Foundation in 2020. It combines research activities on AI and machine learning applications along the entire life cycle of buildings – from planning and construction through to sustainable management.
- The MDSI is also home to the AI Future Lab AI for Earth Observation (AI4EO), which is funded by the Federal Ministry of Research and headed by Xiaoxiang Zhu, one of the five MDSI directors. The AI4EO bundles the strengths of TUM in geodesy and earth observation, satellite technology, mathematics, AI and ethics to develop reliable models of global urbanization, the food supply and the management of natural disasters.
- Under the MDSI roof, TUM is establishing the Center for Digital Medicine and Health as a new research building with federal and state funding. It will position IT competencies within the medical campus of Klinikum rechts der Isar. Under the leadership of MDSI director Daniel Rückert, it will focus on the development of data-driven approaches and AI methods in medicine – from early detection and diagnosis of diseases and the identification of biomarkers for individualized and personalized treatments to ethics, safety and data protection in the use of patient data.
- The Munich Center for Machine Learning (MCML) – under the joint leadership of TUM and LMU – is funded by the Federal Ministry of Education and Research and the HighTech Agenda Bayern as one of the National Centers of Excellence for AI Research. The TUM branch of the MCML is integrated into the MDSI infrastructure under the synergistic leadership of Daniel Cremers, a director of the MDSI. He also heads the TUM side of the partnership of the ELLIS Unit Munich within the European Laboratory for Learning and Intelligent Systems (ELLIS), which is jointly run by TUM with Helmholtz München.
- The Konrad Zuse School of Excellence in Reliable AI – coordinated by TUM and LMU – has received funding from the German Academic Exchange Service (DAAD) since 2022. The MDSI contains the business office of the Konrad Zuse School and is led by MDSI Executive Director Stephan Günnemann. MSc and PhD candidates are trained in the development of reliable AI technologies – including scientific knowledge, business expertise and industry experience. They conduct leading-edge research to prepare AI for use in areas of interest to the public, with all of their impacts on reliability, security and protection of the private sphere.
The MDSI is funded by the Hightech Agenda Bayern (HTA).
Technical University of Munich
Corporate Communications Center
- Julia Rinner
- julia.rinner @tum.de
- presse @tum.de
- Teamwebsite
Contacts to this article:
Prof. Stephan Günnemann
Professur of Data Anlaytics and Machine Learning
s.guennemann@tum.de
https://www.cs.cit.tum.de/daml/startseite/