ERC FP7: Marco Zorzi


PDFProject:  GENMOD - Generative Models of Human Cognition (2008-2013)


Marco Zorzi

 

ERC Grantee: Marco Zorzi

Department: General Psychology

Total Contribution: Euro 492.200,00

Project Duration in months: 60

Start Date: 01/06/2008
End Date: 01/06/2013

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Marco Zorzi is Full Professor of Cognitive Psychology and Artificial Intelligence at the Department of General Psychology and Senior Research Associate at IRCCS San Camillo Neurorehabilitation Hospital (Venice Lido). He completed his Master degree in Psychology (1994) at the University of Padova, his European Diploma in Cognitive Science (2007) at SISSA (International School for Advanced Studies) in Trieste, and his PhD in Experimental Psychology (1999) at the University of Trieste. He was then postdoctoral fellow at University College of London (1994-1998), visiting scientist at Macquarie University in Sydney (1998), postdoctoral fellow at the University of Padova (1999-2000), and obtained a permanent position in 2000, first at the University S. Raffaele in Milan and then at the University of Padova. He leads an interdisciplinary research laboratory (Computational Cognitive Neuroscience Lab) at the frontier between cognitive science, computer science and neuroscience. His research is focused on the computational bases of human cognition, from development to skilled performance and breakdowns of processing following brain damage. This research has been funded by public and private bodies, both national (e.g., the Italian Ministry of Education and Research, Cariparo Foundation, Compagnia di San Paolo Foundation) and international (e.g., European Commission, McDonnell Foundation USA, Wellcome Trust, Royal Society of London). He also coordinates a University of Padova Strategic Grant ("The cognitive neuroscience of attention in perception and cognition"; 2014-2017). His project supported by an ERC Starting grant for frontier research (2008-2013) has exploited recent advances in machine learning (deep unsupervised learning and probabilistic generative models) to develop a new generation of neural network models of learning and cognition. This work has produced realistic, large-scale models that explain key empirical phenomena both at the behavioral and neural level.