BaticlePhD Student (Grant PACA - Cintoo3D)

Topic  Surface reconstruction from dense point clouds

Keywords surface reconstruction, data visualisation, point cloud compression, geometry processing

Arnaud Bletterer obtained his MSc in Computer Science from the University of Strasbourg in 2014. He is currently PhD student at the University of Nice Sophia Antipolis, under the supervision of Marc Antonini and Frédéric Payan. This thesis is funded by the Region "Provence Alpes Côte d'Azur", in collaboration with the startup Cintoo3D, which is a spinoff the the CNRS and the University of Nice Sophia Antipolis. His work mainly focuses on 3D surface reconstruction and data visualisation.

www.i3s.unice.fr/~blettere/

 

 

BaticlePhD student (Cifre 4G-SGME)

Topic  Bio-inspired image coding

Doutsi Effrosyni has received her undergraduate diploma in Computer Science and Biomedical Informatics from University of Central Greece, Lamia, Greece in 2012 (It has recently renamed into University of Thessaly). During the same year she moved in Nice in order to study a full time master program  in Computational Biology and Biomedicine, at University of Nice, Sophia Antipolis (recently renamed as Université Côte d'Azur). At the end of 2013, she started a PhD in Signal and Image Processing under the supervision of Pr. Lionel Fillatre and the research director Marc Antonini at I3S laboratory, Université Côte d'Azur, CNRS. This PhD thesis is founded by an industrial partner, the SGME company, and the ANRT. Based on the needs of SGME, the goal of this thesis is to construct a new codec for natural images which is inspired by the central nervous system.

doutsiefrosini.wix.com/effrosynidoutsi

dannyPhD Student (MESR Grant)

Topic  Statistical machine learning

Danny Schmitt obtained his MSc in mathematics at the University of Nice Sophia-Antipolis in 2014, and then his MSc in computer science in 2016. He started a PhD in statistical learning under the supervision of Lionel Fillatre at I3S laboratory in October 2016. This thesis aims to develop effective learning algorithms when the data are few, with applications on biological measures.