Erik Rodner is a professor of Machine Learning and Data Science at the University of Applied Sciences Berlin (HTW Berlin). His work lies at the intersection of machine learning and computer vision, with a particular interest in learning under limited data availability.
Prior to joining HTW Berlin, he held a tenured lecturer position at the University of Jena (until 2018), where his research focused on transfer and active learning for computer vision, aiming to improve generalization when training data is scarce. He gained earlier international research experience during a postdoctoral stay at the University of California, Berkeley in 2012–2013, where he worked on domain adaptation and open-set recognition.
Between 2018 and 2020, Erik was part of ZEISS Corporate Research as Machine Learning Lead. His team developed learning-based methods for optical inspection as well as medical and microscopy image analysis, bridging academic research and industrial applications.
Erik has authored more than 100 peer-reviewed publications in machine learning and computer vision, published in leading international conferences and journals such as CVPR, ECCV, ICCV, TPAMI, and IJCV. He contributes regularly to the research community as an author and reviewer.