András György received the M.Sc. (Eng.) degree (with distinction) in technical informatics from the Technical University of Budapest, in 1999, the M.Sc. (Eng.) degree in mathematics and engineering from Queen's University, Kingston, ON, Canada, in 2001, and the Ph.D. degree in technical informatics from the Budapest University of Technology and Economics in 2003.
He was a Visiting Research Scholar in the Department of Electrical and Computer Engineering, University of California, San Diego, USA, in spring of 1998. In 2002-2011 he was with the Computer and Automation Research Institute of the Hungarian Academy of Sciences, where, from 2006, he was a Senior Researcher and Head of the Machine Learning Research Group. In 2003-2004 he was also a NATO Science Fellow in the Department of Mathematics and Statistics, Queen's University. He also held a part-time research position at GusGus Capital Llc., Budapest, Hungary, in 2006-2011. In 2012-2015, he was a researcher in the Department of Computing Science, University of Alberta, Edmonton, AB, Canada. In 2015-2019, he was a Senior Lecturer at the Department of Electrical and Electronic Engineering of Imperial College London, London, UK. Since 2018, he has been a Research Scientist at Deepmind, London, UK.
His research interests include machine learning, statistical learning theory, online learning, adaptive systems, information theory, and optimization.
Dr. György received a best paper award at the 7th IEEE Global Conference on Signal and Information Processing (GLOBALSIP2019) in 2019, the Gyula Farkas prize of the János Bolyai Mathematical Society in 2001 and the Academic Golden Ring of the President of the Hungarian Republic in 2003.
- Theory of Reinforcement Learning, Fall 2020. Visiting Scientist.