Prof. Dr.-Ing. Klaus Diepold
Academic Career and Research Areas
Prof. Diepold (b. 1961) conducts research into audio and video signal processing and coding, assessment of subjective video quality (quality of experience), multivariate data analysis and data mining. His other interests are design of fast algorithms based on time-varying system theory and computational linear algebra, machine learning for cognitive systems (CoTeSys), directional hearing and 3D sound synthesis for robotics and telepresence systems and technology transfer.
After studying electrical engineering and information technology and completing his doctorate (1992) at TUM, Prof. Diepold worked as a scientist and entrepreneur in the video and television industry in Munich and New York. For over 10 years, he played an active role in the development of MPEG standards (MPEG-4, MPEG-A). In 2002, he accepted the Chair of Data Processing at TUM. Between 2005 and 2010, he held the position of Dean of Studies. He was a visiting professor at the University of Alberta, Canada and NICTA, Australia. Prof. Diepold is a member of the supervisory board of the Center for Digital Technology and Management (CDTM) and the “Cognition for Technical Systems” (CoTeSys) cluster of excellence. From 2013 to 2015 Prof. Diepold served as Senior Vice President for Diversity and Talent Management.
- Fellowship for Innovation in University Teaching, Stifterverband für die Deutsche Wissenschaft (2012)
- Certificate of Appreciation, ISO/IEC for work on MPEG-A (2008)
- TUM Teaching Prize (2004)
- Award for most innovative software product for “MotionPerfect” (2001)
- Best Paper Award Society for Information Technology (ITG) (1992)
Hawe S, Kleinsteuber M, Diepold K: "Dense disparity maps from sparse disparity measurements". Proceedings 13th International Conference on Computer Vision. Barcelona, 6-3 Nov 2011.Abstract
Günther J, Pilarski PM, Helfrich G, Shen H, Diepold K. "Intelligent laser welding through representation, prediction, and control learning: An architecture with deep neural networks and reinforcement learning". Mechatronics. 2016; 34: pp.1-11.Abstract
Hossfeld T, Keimel C, Hirth M, Gardlo B, Habigt J, Diepold K, Tran-Gia P: "Best practices for QoE crowdtesting: QoE assessment with crowdsourcing". IEEE Transactions on Multimedia. 2014; 16(2): 541-58.Abstract
Hawe S, Kleinsteuber M, Diepold K: "Analysis operator learning and its application to image reconstruction". IEEE Transactions on Image Processing. 2013; 22(6): 2138-50.Abstract
Hawe S, Kleinsteuber M, Diepold K: "Dense disparity maps from sparse disparity measurements". Proceedings 13th International Conference on Computer Vision. Barcelona. 6-3 Nov 2011.Abstract