Manuel Günther, Prof. Dr.
- Artificial Intelligence and Machine Learning
Prof. Dr. Manuel Günther started his position as Assistant Professor for Artificial Intelligence and Machine Learning at the Department of Informatics of the University of Zurich in July 2020. He received his diploma in Computer Science with a major subject of machine learning from the Technical University of Ilmenau, Germany, in 2004. His doctoral thesis was written in the Institut für Neuroinformatik at the Ruhr University of Bochum, Germany, between 2004 and 2011 about statistical extensions of Gabor graph based face detection, recognition and classification techniques. Finally, he received his doctoral degree (Dr.-Ing.) from the Technical University of Ilmenau in 2012.
Between 2012 and 2015, Prof. Günther was a postdoctoral researcher in the Biometrics Group at the Idiap Research Institute in Martigny, Switzerland. Since then, he is actively participating in the implementation of the open source signal processing and machine learning library Bob. He was the leading developer of the Biometric Recognition packages, a library to run biometric recognition experiments, which he presented as a hands-on tutorial at the International Joint Conference on Biometrics, 2017.
From 2015 to 2018, Prof. Günther was working as a Research Associate at the Vision and Security Technology Lab at the University of Colorado Colorado Springs, Colorado, USA. There, he developed algorithms for the alignment-free classification of facial attributes from single images. His research also included the classification of samples under the presence of unknown classes. Particularly, he developed algorithms for the open-set identification of human faces, which he applied in two Unconstrained Face Detection and Open Set Recognition Challenges, which he was leading himself.
After a short industry excursion at the trinamiX GmbH in Ludwigshafen, Germany, Prof. Günther accepted a call as Assistant Professor at the Department of Informatics. There, he is continuing his research on the classification of the unknown and on automatic face recognition. His research interests also include deep learning in general and the phenomenon of adversarial samples in particular. Furthermore, he promotes the idea of Reproducible Research, which allows researchers to start their work at the state of the art.