VASCON Sebastiano

Qualifica
Ricercatore
Telefono
041 234 7549
E-mail
sebastiano.vascon@unive.it
SSD
INFORMATICA [INF/01]
Sito web
www.unive.it/persone/sebastiano.vascon (scheda personale)
Struttura
Dipartimento di Scienze Ambientali, Informatica e Statistica
Sito web struttura: https://www.unive.it/dais
Sede: Campus scientifico via Torino (edificio Alfa)
Stanza: studio Assegnisti B1 (edificio Zeta B)
Struttura
Centro Europeo Interuniversitario di Ricerca - European Center for Living Technology
Sito web struttura: https://www.unive.it/eclt
Sede: Palazzo Minich
Research Institute
Research Institute for Digital and Cultural Heritage
Research Institute
Research Institute for Complexity

Sebastiano Vascon is an assistant professor (RTDa) at Ca' Foscari University of Venice at the Department of Environmental Sciences, Computer Science and Statistics (DAIS). He is also affiliated to the European Centre for Living Technology and he holds a research visiting position at the Technical University of Munich in the Dynamic Vision and Learning Group. 
In May 2016 he obtained the title of Ph.D. from the Italian Institute of Technology and the University of Genoa, with a thesis on evolutionary game theory applied in the context of pattern analysis and computer vision. During the doctorate period, he has focused his efforts on the detection of conversational groups to improve video surveillance systems and in the study of brain connectomics for the automatic analysis of neuronal connections between the hemispheres. Besides, he spent a visiting period at the ETH of Zurich working on multiple objects tracking and automatic video segmentation. In 2009 and 2012 he received from the Ca’ Foscari University of Venice the bachelor and the master degree in Computer Science. Furthermore, during the master, he spent an exchange period at the University College of London, where he deepened his interests in the field of computer vision and artificial intelligence. Currently, his research interests are focused on the automatic analysis of patterns, computer vision, machine learning and graph theory using models inspired by the game theory. Recently he found a renewed interest in neural networks (mainly deep-learning) and learning problems with contextual constraint.