“Machine-learning models for inverse problem: From theory to application”

Topics of Interest
  • Mathematics of data science and machine learning
  • Generative models in machine learning
  • Variational methods in imaging
  • Dynamic and multi-modality inverse problems
  • Model-based regularization
  • Biomedical imaging
  • Image and video decompression
Short CV

Martin Holler received his MSc (2010) and his PhD (2013) with a “promotio sub auspiciis praesidentis rei publicae” in Mathematics from the University of Graz. After research stays at the University of Cambridge, UK, and the Ecole Polytechnique, Paris, he currently holds an Assistant Professor position at the Institute of Mathematics and Scientific Computing of the University of Graz, where he is leading a research group on “Mathematics of Data Science”. His research interests include machine learning techniques for inverse problems and imaging, generative models, variational regularization as well as applications in biomedical imaging, image compression and beyond.