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
- Tercan, H., Bitter, C., Bodnar, T., Meisen, P., & Meisen, T. (2021). "Evaluating a Session-based Recommender System using Prod2vec in a Commercial Application" in Proceedings of the 23rd International Conference on Enterprise Information Systems , SciTePress 610—617.
ISBN: 978-989-758-509-8
- Maack, R. F., Tercan, H., Solvay, A. F., Mieth, M., & Meisen, T. (2021). "Fault Detection in Railway Switches using Deformable Convolutional Neural Networks" in 2021 IEEE 19th International Conference on Industrial Informatics (INDIN) , IEEE 1—6.
ISBN: 978-1-7281-4395-8
- Steiniger, Y., Kraus, D., & Meisen, T. (2021). "Generating Synthetic Sidescan Sonar Snippets Using Transfer-Learning in Generative Adversarial Networks" , Journal of Marine Science and Engineering , 9 (3), 239.
- Pol, S., Baer, S., Turner, D., Samsonov, V., & Meisen, T. (2021). "Global Reward Design for Cooperative Agents to Achieve Flexible Production Control under Real-time Constraints" in Proceedings of the 23rd International Conference on Enterprise Information Systems , SciTePress 515—526.
ISBN: 978-989-758-509-8
- 2020
- Baer, S., Turner, D. C., Mohanty, P. K., Samsonov, V., Bakekeu, J. R., & Meisen, T. (2020). "Multi Agent Deep Q-Network Approach for Online Job Shop Scheduling in Flexible Manufacturing" in Proceedings of the 7th International Conference on Industrial Engineering and Applications (ICIEA) .
