As further core elements of Cyber Valley, ten new professorships will be established over the next few years at the Eberhard Karls University of Tübingen and the University of Stuttgart, some of which will be financed by endowments. Initially, two new endowed chairs were recently established at the Universities of Stuttgart and Tübingen:
University of Tübingen: Professorship for Machine Learning - sponsored by Robert Bosch GmbH
In June 2018, Matthias Hein assumed an endowed professorship at the University of Tübingen, which Bosch will finance with 5.5 million euros over the next ten years as part of its Cyber Valley commitment. Hein's research focuses on methods of machine learning, in particular the deep learning approach. In the process, software processes information - inspired by the human brain - in hierarchically organized neural networks in which the degree of abstraction increases from layer to layer. The decisive difference to other approaches of machine learning is that engineers do not specify how the degree of abstraction increases from layer to layer. Rather, the hierarchical structure organizes itself from the data itself using a universal learning process. Using training examples, programs learn to identify people or objects in images and to interpret entire image scenes. Using such a deep learning algorithm, the "AlphaGo" software recently succeeded for the first time in beating a top player in the Go board game whose computers had previously failed because of their complexity.
Matthias Hein has been teaching mathematics and computer science at Saarland University since 2011. He studied physics in Tübingen and received his doctorate in computer science from the University of Darmstadt. From 2002 to 2007, he was part of Professor Bernhard Schölkopf's research group at the Max Planck Institute for Biological Cybernetics. Today, Schölkopf heads the Max Planck Institute for Intelligent Systems in Tübingen and is one of the world's leading scientists in the field of machine learning.
University of Tübingen: Professor for the Methods of Machine Learning
Philipp Hennig was appointed professor for "Methods of Machine Learning" in the Department of Computer Science in May 2018. There he researches calculation algorithms for artificial intelligence. "Learning machines have done impressive things in the recent past," says Hennig. "But they are currently wasting resources. Rich Internet companies can afford the necessary mainframe computers and highly paid personnel as watchdogs. In order to make machine learning usable for small and medium-sized businesses and in areas such as machine and vehicle construction or on mobile phones, algorithms must become faster, more reliable and more user-friendly. This includes controllability: If an AI component of a complex overall system gets out of hand, a red lamp must light up somewhere". Using funds from an ERC grant from the European Commission, Hennig's colleagues are, for example, researching methods with which the "training" of learning computer programs can be automated and monitored.
Hennig studied physics at the University of Heidelberg and at Imperial College in London and received his doctorate in 2011 from the University of Cambridge on "Approximate Inference in Graphical Models". He has been doing research at the Max Planck Institute for Intelligent Systems in Tübingen since 2011 and initially worked in the "Empirical Inference" department of AI researcher Professor Bernhard Schölkopf. From 2015 he headed his own research group, with which he established the concept of "Probabilistic Numerics", a mathematical theory of uncertainty in computer calculations. Among other things, the group is investigating how fast and highly simplified calculation steps can contribute to solving complex calculation tasks and how computers can "keep track" of the effects of calculation errors and simplifications.
Daimler AG will establish the endowed chair at the University of Stuttgart on the topic of entrepreneurship in digital transformation.