Thomas Pock serves as Head of the Institute of Visual Computing, Vice Dean of the Faculty of Computer Science and Biomedical Engineering, and Co-Director of BioTechMed-Graz.
Thomas Pock serves as Head of the Institute of Visual Computing, Vice Dean of the Faculty of Computer Science and Biomedical Engineering, and Co-Director of BioTechMed-Graz.
Thomas Pock received his MSc (2004) and PhD (2008) in Computer Engineering (Telematik) from Graz University of Technology. After a PostDoc at the University of Bonn and the Hausdorff Center of Mathematics, he returned to Graz University of Technology as an Assistant Professor at the Institute of Computer Graphics and Vision.
In 2013, he was awarded the START Prize by the Austrian Science Fund (FWF) for his project BIVISION: Bilevel Optimization for Computer Vision, and in the same year received the German Pattern Recognition Award (DAGM). In 2014, he obtained an ERC Starting Grant for his project HOMOVIS: Higher Order Models for Computer Vision. Since 2014, he has been Professor of Computer Science at Graz University of Technology, where he leads the Vision, Learning and Optimization (VLO) group.
Thomas Pock has served as associate editor for leading vision journals, including the Journal of Mathematical Imaging and Vision, the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the SIAM Journal of Imaging Sciences. He has also been an area chair for major computer vision conferences (ICCV, ECCV, CVPR).
From 2019 to 2024, he was a member (reporter) of the board of the Austrian Science Fund (FWF) responsible for computer science. Since 2023, he has been head of the Institute of Computer Graphics and Vision which in 2025 transformed into the newly founded Institute of Visual Computing. Since 2024, he has served as Vice Dean of the Faculty of Computer Science and Biomedical Engineering, and since 2025 as co-director of BiotechMed-Graz.
His research interests include image processing, computer vision, inverse problems, convex and non-smooth optimization, sampling algorithms, and machine learning.