András Horváth, PhD is an Associate Professor at the Faculty of Information Technology and Bionics at Pázmány Péter Catholic University. His research is mostly focused on computer vision and artificial intelligence, especially on the efficient implementation of modern machine learning algorithms with emerging devices. He took part in the DARPA-UPSIDE (Unconventional Processing of Signals for Intelligent Data Exploitation) project between 2012 and 2018 in a consortium with Intel, MIT, which aimed the development of an object recognition pipeline with oscillatory based computing, implemented on emerging devices (e.g.: spin-torque and resonant body oscillators) and was involved in multiple international research grants sponsored by the European Union and ONR. He is author or co-author of more than 50 publications which appeared in various international journals. He is an active Reviewer for various peer reviewed journal papers. (e.g.: IEEE Transaction on Signal Processing, IEEE Transactions on Circuits and System, etc.). He is a member of the IEEE Circuits and System Society and the IEEE Computational Intelligence Society and the secretary of the Cellular Nanoscale Networks and Array Computing Technical Committee.


  • • Szentannai, Kálmán, Jalal Al-Afandi, and András Horváth: "MimosaNet: An Unrobust Neural Network Preventing Model Stealing." CVPR 2019 Adversarial Machine Learning in Real-World Computer Vision Systems, 2019.
  • • Makra, Á., Bost, W., Kalló, I.,Horváth, A., Fournelle, M., Gyöngy, M.: “Enhancement of Acoustic Microscopy Lateral Resolution: A Comparison Between Deep Learning and Two Deconvolution Methods”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(1), 2019.
  • • Barna, László and Dudok, Barna and Miczán, Vivien, András Horváth, László, Zsófia I and Katona, István: “Correlated confocal and super-resolution imaging by VividSTORM”, Nature protocols, 2016.
  • • Janka, Hatvani, András Horváth, Michetti, Jérôme, Basarab Adrian, Kouamé Denis and Gyöngy, Miklós: “Deep Learning-Based Super-Resolution Applied to Dental Computed Tomography”, Transansactions on Radiation and Plasma Medical Sciences, 2018.
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