Prof. Dr. Markus Lange-Hegermann shares his knowledge at the “Symposium on Applied Mathematics”

On October 11, 2023, an internal event of Bosch’s pre-development department took place at the Bosch Research Campus in Renningen. The well-attended event provided ample opportunity to exchange ideas and insights – both through exciting presentations and inspiring conversations in a pleasant atmosphere.

Prof. Dr. Markus Lange-Hegermann, board member from the Institute for Industrial Information Technology (inIT), professor at the Technische Hochschule Ostwestfalen-Lippe (TH OWL) and team member of the SAIL-Project, made an important contribution to the event with his keynote lecture “Symposium on Applied Mathematics”. 
In his 45-minute talk, he addressed the fascinating question of how algorithms from the field of machine learning can be used to describe the solution set of linear differential equations. In doing so, he made clear that linear differential equations are of crucial importance in various application areas. As examples, he cited control engineering, modeling heat propagation in materials, and explaining electromagnetic phenomena.

The presentation was followed by positive feedback and lively discussions
The reactions to the lecture were extremely positive. Prof. Dr. Markus Lange-Hegermann impressed all listeners with his expertise as well as his deep understanding of this exciting and complex topic. The interest was so great that a lively discussion about the lecture started immediately afterwards, with a total of more than two dozen questions.  

Close links between research and industry
Although Prof. Lange-Hegermann is currently working closely with Bosch as part of a research semester, it is important to emphasize that the symposium did not take place in the direct context of this research semester. However, it illustrates the close link between academic research and industrial practice.
In his role as a board member at inIT, Prof. Dr. Markus Lange-Hegermann is intensively dedicated to the field of probabilistic machine learning and is part of the SAIL-Project. His research work contributes significantly to the further development of this field.