I offered the lectures on Probabilistic Graphical Models in Computer Vision.

My main research interests include semantic image segmentation and image registration. Recently I have mainly focused on developing semantic segmentation methods via probabilistic graphical models (e.g., Conditional Random Fields) and deep learning.

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Csaba Domokos received the M.S. degree in Computer Science, the M.S. degree in Informatics and the M.S. degree in Mathematics from the University of Szeged, Hungary in 2004, 2006 and 2010, respectively. He made his Ph.D. research under the supervision of Prof. Zoltan Kato and received the Ph.D. degree (summa cum laude) from the University of Szeged in 2011. He spent two years (2012-2014) as a Research Fellow in the Learning and Vision Research Group, headed by Prof. Shuicheng Yan, at the National University of Singapore. He worked for a year as a post-doctoral researcher in the Computer Vision and Machine Learning Group, directed by Prof. Christoph H. Lampert at the IST Austria. Since 2015 he is an Alexander von Humboldt Fellow (postdoc) in the Computer Vision Group, directed by Prof. Dr. Daniel Cremers, at the Technical University of Munich.

Please visit my webpage https://sites.google.com/site/cdomokosres/