Prof. Dr. Benjamin Risse
"No positions via CiM-IMPRS in 2024"

Interdisciplinary Machine Learning and Computer Vision Research

Colliding and overlapping objects are a common problem in biomedical image analysis. Here, we are using a novel imaging technology in combination with computer vision and machine learning approaches to resolve colliding Drosophila larvae.
© Risse

Computer Science
Imaging Technologies
Mathematics / Mathematical Modelling / Image Analysis
Machine Learning
Computer Vision

We are interested in interdisciplinary research questions involving the development of novel imaging, computer vision and machine learning technologies yielding new approaches to acquire and analyse data with applications in medicine, biology and ecology. This requires a fundamental investigation of how computers perceive and understand complex real-world situations. By examining the entire process from data acquisition (i.e. sensing hardware) to quantitative evaluation (i.e. algorithms) we are seeking for solutions beyond classical image processing, pattern recognition and artificial intelligence methodologies. Many lessons remain to be learned to tackle the challenges of real-world data which we seek to study and reveal in the coming years to enable and build novel data-driven systems.

Prof. Dr. Benjamin Risse
University of Münster
Faculty of Mathematics and Computer Science, Computer Science Department

Einsteinstr. 62
48149 Münster

T: +49 (0) 251- 83 - 32717
F: +49 (0) 251- 83 - 33755
b.risse@uni-muenster.de

Vita

2005 - 2010: Diploma degree in computer science with a minor in biology , University of Münster
2011 - 2015: Ph.D. studies in Computer Science (Prof. Jiang) and Neurobiology (Prof. Klämbt), University of Münster
2012: Visiting Researcher at Sichuan University, China
2015 – 2017: PostDoc at the School of Informatics (Prof. Webb), University of Edinburgh
2017 – 2018: PostDoc at the University of Münster (Prof. Jiang), University of Münster
Since 2018: W1 Professor at the University of Münster (Computer Vision and Machine Learning Systems Group)

Selected references

Risse, B., Thomas, S., Otto, N., Löpmeier, T., Valkov, D., Jiang, X. and Klämbt, C., (2013). FIM, a novel FTIR-based imaging method for high throughput locomotion analysis. PloS one, 8(1).

Otto, N., Risse, B., Berh, D., Bittern, J., Jiang, X. and Klämbt, C., (2016). Interactions among Drosophila larvae before and during collision. Scientific reports, 6(1), pp.1-11.

Risse, B., Mangan, M., Del Pero, L. and Webb, B., (2017). Visual tracking of small animals in cluttered natural environments using a freely moving camera. In Proceedings of the IEEE International Conference on Computer Vision Workshops (pp. 2840-2849).

Otto, N., Marelja, Z., Schoofs, A., Kranenburg, H., Bittern, J., Yildirim, K., Berh, D., Bethke, M., Thomas, S., Rode, S. and Risse, B.,(2018). The sulfite oxidase Shopper controls neuronal activity by regulating glutamate homeostasis in Drosophila ensheathing glia. Nature communications, 9(1), pp.1-12.

Haalck, L., Mangan, M., Webb, B. and Risse, B., (2020). Towards image-based animal tracking in natural environments using a freely moving camera. Journal of neuroscience methods, 330, p.108455.

Links

Risse Lab