Hubert Welp

Hubert Welp serves as Professor of Applied Computer Science at Georg Agricola University of Applied Sciences since 2004, concurrently holding leadership roles as Head of the Information Technology and Digitalization Bachelor Program and Head of the Software Engineering Laboratory. His academic foundation in physics transitions into applied computing through industry experience at IBM and MedScience GmbH.

His educational trajectory includes:

• Physics studies at University of Münster (1984-1988)
• Diploma research in laser spectroscopy at University of Marburg (1988-1990)
• Doctoral work on “Laser-induced perturbations in Doppler distributions” at University of Marburg (1991-1994)

Research focuses on Software Engineering, Pattern Recognition, and Optical Coherence Tomography (OCT), with significant contributions to biomedical imaging (brain tumor analysis, peripheral nerve visualization) and industrial applications (RFID systems, non-destructive testing). His work integrates applied pattern recognition with spectroscopic OCT for tissue differentiation and material analysis, evidenced by 30+ publications since 2010. As member of both German Physical Society and German Computer Science Society, he bridges theoretical physics with practical computing solutions.

Publication analysis (2013-2019) reveals three dominant research vectors: (1) Advanced OCT techniques for medical diagnostics, particularly brain tissue classification using texture features; (2) Industrial adaptation of OCT for coating analysis in conservation science; (3) Algorithmic innovations in spectroscopic data processing. Collaborative work with Martin Hofmann’s group demonstrates consistent output in optics journals and SPIE proceedings, with recent emphasis on GPU-accelerated analysis and ex vivo tumor detection.

Directing the Software Engineering Laboratory, Welp oversees development of OCT data processing frameworks and RFID applications. While specific grants aren’t detailed, sustained publication output indicates active research funding cycles. His teaching portfolio in programming, data networks, and applied pattern recognition directly informs laboratory research directions.