idw – Informationsdienst Wissenschaft
Nachrichten, Termine, Experten
Nachrichten, Termine, Experten
idw - Informationsdienst
Wissenschaft
With its new Mathematical Institute for Machine Learning and Data Science (MIDS) the Catholic University of Eichstätt-Ingolstadt (KU) wants to contribute to academically mining the potential of digitalization and to convey to young people the basics of artificial intelligence and machine learning. The top-class scientists working at the MIDS conduct research in the fields of climate and weather simulation, data science, deep learning and the associated mathematical foundations. The Ingolstadt-based institute will be supported for several years by the city of Ingolstadt who has established two endowed chairs for this purpose.
At the presentation of the new institute, Prof. Dr. Jens Hogreve, Vice President for Research at the KU said: “We see digitalization as a cross-cutting issue in order to contribute to a human-centered digital society. In this, it is central to also establish our own expertise in the field of applied mathematics, which in turn serve as a basis for the application and reflection of data science and artificial intelligence.” The City of Ingolstadt’s business consultant, Prof. Dr. Georg Rosenfeld said: “This new institute backs our city’s endeavor to sharpen its profile as a location of excellent research and teaching when it comes to user-oriented and responsible digitalization. We are pleased to see our commitment already bearing fruit and attracting renowned researchers as well as additional external funding.”
One of the research focus areas of the MIDS is the fundamentals of machine learning. Nowadays, machine learning algorithms achieve near-human performances or even occasionally manage to out-perform humans in many applications. Well-known examples are image recognition (e.g. tumor detection in medicine), speech recognition or autonomous vehicles. These advances are owed to the fact that nowadays deep neural networks can be trained successfully, thanks to our enormous computing power and available data volumes. “Despite this immense practical success, we still don’t have a comprehensive theoretical understanding of why these methods work so well,” says Prof. Dr. Götz Pfander, spokesman of the Institute and holder of the Chair of Mathematics/Computational Science at the KU. In addition, tests have repeatedly shown that trained neural networks are often not very robust and that even minimal changes to the input, which are invisible to humans, can produce incorrect output. It is therefore essential to analyze the reasons for this instability, he said. "Existing guarantees of success for machine learning are too weak to explain their practical success so far. It is of great interest - especially for critical applications such as in medicine - to develop improved guarantees on a mathematical basis," Pfander said.
Another focus of the MIDS is the processing of data to predict environmental developments - for example, for weather forecasts and climate or soil research. The advancement of the mathematical foundations of these methods is essential in this process and is highly relevant to a wide range of applications in science and industry. Besides linking physical correlations with limited data, there is another challenge in modeling such complex systems: They consist of parts that occur at different scales and times. For example, weather ranges from the formation of a single snowflake to the progression of annual mean temperatures. However, a computer simulation must always be limited to a part of the scales that exist in reality - any scales smaller or larger than that have to be modeled. That is why at MIDS, researchers are working on mathematical methods to model and simulate such multi-scale problems.
In designing such simulations, the MIDS also wants to take the aspect of sustainability into account. This is important, as complex algorithms use up immense computing power, leading to high energy consumption. Addressing this problem is a core aim of the joint project “Resource Aware Artificial Intelligence for Future Technologies”. In it, Professor Dr. Felix Voigtlaender from the Chair of Reliable Machine Learning at the KU is working cooperatively with FAU Erlangen-Nürnberg, TU München and Universität Bayreuth. This Chair is also located at the MIDS and is funded by the Bavarian Hightech Agenda. The KU won the chair in a competition of the Bavarian State for the establishment of new professorships on artificial intelligence.
The MIDS professors also form the expert core of the new Bachelor degree program „Data Science“, which is to start in the winter semester at the KU. This program aims at education highly qualified professionals for industry and research, who will contribute their expertise in a responsible and discreet manner. That is why the program teaches students the basics of machine-learning and other current methods efficiently by applying cutting-edge software technologies.
Information on the MIDS can be found at http://www.ku.de/mids, for details on the new Bachelor’s degree program „Data Science“, please go to http://www.ku.de/ds.
Short portraits of the MIDS professors
Prof. Dr. Tijana Janjic is Heisenberg professor for data assimilation at the KU and a board member of the DFG special research area TRR 165 “Wellen, Wolken, Wetter” (waves, clouds, weather). In order to better be able to forecast weather extremes or the melting of arctic ice, information in the form of heterogenous data must be combined with numeric models of dynamic systems. This is done by data assimilation enabling a closer analysis of processes and forecast of their trends. In the field of data assimilation, the professorship is concerned with the further development of data science algorithms by incorporating physical conservation laws and solving correspondingly large optimization problems in the environmental sciences. The quantification of uncertainties of predictions, numeric models and observations also play a key role.
Prof. Dr. Marcel Oliver holds the Chair of Applied Mathematics endowed by the City of Ingolstadt. He works on the simulation and modeling of complex multi-scale systems such as those encountered in climate research, but also in the materials and life sciences. Despite exponentially increasing computing power, the differences between the smallest and the largest relevant phenomena are often so extreme that scientific progress can only be achieved through better algorithms, better parametrizations - the simplified descriptions of small-scale processes - and the increased use of data. The approaches pursued by the research group include structure-preserving and energy-consistent algorithms, mathematical methods of model reduction, and dynamical, stochastic, or data-based models to represent small-scale processes that cannot be represented numerically.
Prof. Dr. Götz Pfander holds the Chair for Mathematics/Computational Science at the KU and is the speaker of MIDS. He analyzes and develops methods for the measurement, analysis and digitization of continuous signals, images and operators. His research focuses on so-called sampling theory of signals and operators as well as compressed sensing, which is used to reduce the number of measurements necessary to reconstruct a signal - such as an image in computer tomography - from the measurements.
Junior professor Dr. Dominik Stöger holds one of seven junior professorships that are part of the KU tenure-track program for the promotion of young researchers. The program is being funded by a federal states and government program with about five million euros. The framework for interdisciplinary exchange between the seven individual junior professorships is provided by a concept on the topic of “A human-centered digital society” that they have in common. Professor Stöger’s research is concerned with the development and analysis of mathematical methods in machine learning and signal processing - for example, with regard to mathematical principles on the question of how much data is really needed for a valid and reliable statement.
Prof. Dr. Felix Voigtlaender holds the Chair for Reliable Machine Learning funded by the HighTech Agenda of Bavaria. His research is funded by the Emmy Noether-Programm of the German Research Foundation (DFG). Professor Voigtlaender’s chair researches the mathematical analysis of machine learning algorithms. Of special interest in this regard are modern algorithms of deep learning, more precisely, the issue of stability or robustness of deep learning algorithms.
Prof. Dr. Nadja Ray will take on another chair endowed by the City of Ingolstadt. The focus of her chair is on geomantics and geomathematics. This includes expertise in inverse problems, modeling in the geosciences, geomonitoring based on mathematical-statistical methods and the analysis and visualization of spatial data and their integration in geographic information systems. The latter is the foundation for long-term planning in the fields of traffic, infrastructure and natural hazard prevention.
http://www.ku.de/mids
http://www.ku.de/ds
(from left) Prof. Dr. Felix Voigtlaender, business consultant Prof. Dr. Georg Rosenfeld, MIDS spokes ...
Dr. Christian Klenk
Klenk/upd
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