David Rohrschneider

David Rohrschneider has been an Academic Associate and PhD student at the Institute of Computer Science at Hochschule Ruhr West (HRW) since 2024. His research focuses on machine vision, multimodal neural networks, transfer learning methods, and the integration of digital product passports with artificial intelligence to promote the circular economy.

As part of his work on the Circular Performer Emscher-Lippe and Transferhub im Prosperkolleg projects, he supports regional companies on their path towards a digital and circular future.

The best way to reach me is by email at david.rohrschneider@hs-ruhrwest.de

PhD College NRW

Supporting regional companies in implementing digital product passports and applying AI-driven circular economy practices

• Prof. Dr.-Ing. Uwe Handmann
• Prof. Dr.-Ing. Anselm Haselhoff

• Circular Performer Emscher-Lippe 
• Transferhub im Prosperkolleg

• Multimodal Neural Networks
• Digital Product Passport
• Machine Vision
• Transfer Learning Methods

Neuroinformatics Laboratory
 

• Training AI models for object recognition
• Local deployment and evaluation of large language models
• Web applications for demonstrating digital product passports

• Abou Baker, N., Rohrschneider, D., & Handmann, U. (2024). Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks. Machine Learning and Knowledge Extraction, 6(4), 2783-2807.
• Rohrschneider, D., Baker, N. A., & Handmann, U. (2023). Double Transfer Learning to Detect Lithium-Ion Batteries on X-Ray Images. In International Work-Conference on Artificial Neural Networks (pp. 175-188). Cham: Springer Nature Switzerland.
• Deterding, J., Janzen, N., Rohrschneider, D., Lösch, P., & Jansen, M. (2023). Performance Evaluation of Quantum-Resistant Cryptography on a Blockchain. In International Congress on Blockchain and Applications (pp. 124-133). Cham: Springer Nature Switzerland.
• Abou Baker, N., Rohrschneider, D., & Handmann, U. (2022). Battery detection of XRay images using transfer learning. In European Symposium of Artificial Neural Networks.