Prof. Dr.-Ing. Tobias Meisen
Professor of Technologies and Management of Digital Transformation
Area of Research:
- Deep and Machine Learning
- Deep Reinforcement Learning
- Explainable and Transparent Artificial Intelligence
- Knowledge Graphs
- Semantic Interoperability
Biography
Tobias Meisen is Professor of the Institute for Technologies and Management of Digital Transformation (TMDT) at the University of Wuppertal since September 2018. He is also chair of the Interdisciplinary Center for Data Analytics and Machine Learning (IZMD) and has been a founding ambassador of the School of Electrical, Information and Media Engineering since October 2018. He is also co-founder of Hotsprings GmbH, which is now a part of umlaut.
In his daily work, Tobias Meisen is dedicated to digital transformation, especially modern information management in a networked, digital world. The focus of his research is the conceptual design, development and implementation of autonomous technical systems with a focus on deep learning and machine learning. In his second research area, he is dedicated to the collection and integration of digital data with a special focus on the evolutionary construction and management of Knowledge Graphs.
Tobias Meisen studied computer science with a specialization in data mining as well as data exploration and management and holds a PhD in engineering. From October 2015 to August 2018, Tobias Meisen was a junior professor at RWTH Aachen University. He contributed his research results here as part of the Cluster of Excellence "Integrative Production Technology for High-Wage Countries". In March 2010, he was awarded the Young Researcher Award as part of the first funding phase of the Excellence Initiative. Tobias Meisen is co-author and author of more than one hundred scientific publications and regularly serves as a reviewer for various conferences and journals. In recent years, he and his team have successfully supported a large number of research and development projects with partners from research and industry.
Publications
- 2021
- Böhme, O., & Meisen, T. (2021). "Predicting the Progress of Vehicle Development Projects: An Approach for the Identification of Input Features" in Proceedings of the 13th International Conference on Agents and Artificial Intelligence , SciTePress 522—530.
ISBN: 978-989-758-484-8
- Langer, T., & Meisen, T. (2021). "Visual Analytics for Industrial Sensor Data Analysis" in Proceedings of the 23rd International Conference on Enterprise Information Systems , SciTePress 584—593.
ISBN: 978-989-758-509-8
- Samsonov, V., Kemmerling, M., Paegert, M., Lütticke, D., Sauermann, F., Gützlaff, A., Schuh, G., & Meisen, T. (2021). "Manufacturing Control in Job Shop Environments with Reinforcement Learning" in Proceedings of the 13th International Conference on Agents and Artificial Intelligence , SciTePress 589—597.
ISBN: 978-989-758-484-8
- Ekeris, T., Meyes, R., & Meisen, T. (2021). "Discovering Heuristics And Metaheuristics For Job Shop Scheduling From Scratch Via Deep Reinforcement Learning" in Proceedings of the 2nd Conference on Production Systems and Logistics (CPSL~2021) .
- Alves-Gomes, M., Tercan, H., Bodnar, T., Meisen, T., & Meisen, P. (2021). "A Filter is Better Than None: Improving Deep Learning-Based Product Recommendation Models by Using a User Preference Filter" in 2021 IEEE 23rd Int. Conf. on High Performance Computing and Communications; 7th Int. Conf. on Data Science and Systems; 19th Int. Conf. on Smart City; 7th Int. Conf. on Dependability in Sensor, Cloud and Big Data Systems and Application (HPCC/DSS/SmartCity/DependSys) . 1278—1285.
