Federico Monti

 

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Institute of Computational Science
Faculty of Informatics
Via Giuseppe Buffi 13
6900 Lugano
Switzerland
 
 
 

Federico Monti 

Federico Monti is a PhD student under the supervision of prof. Michael Bronstein, he moved to Università della Svizzera italiana in 2016 after achieving cum laude his B.Sc. and M.Sc. in Computer Science and Engineering at Politecnico di Milano. His research currently deals with the emerging field of Geometric Deep Learning and, in particular, with generalisation of Convolutional Neural Networks (CNNs) for signals defined on manifolds and graphs.
GDL currently represents one of the latest and most active research fields in Machine Learning thanks to broad applicability and novelty it presents. Possible applications of GDL techniques range from Recommendation Systems (e.g. Matrix Completion problems), to Biomedical solutions (e.g. Disease Predictions), to Shape Analysis approaches (e.g. Shape Correspondence).

News

September, 2017

  • Our paper "Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks" has been accepted at NIPS2017.
 

July, 2017

  • Intern at Google (July - October 2017).
 

June, 2017

 

May, 2017

  • We present a new spectral graph CNN: CayleyNet. [PDF]
 

April, 2017

  • New arXiv on "Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks". [PDF]
 

February, 2017

  • Reviewer for ICCV2017.
  • Our paper "Geometric deep learning on graphs and manifolds using mixture model CNNs" (MoNet) has been accepted at CVPR2017 (oral presentation).
 

November, 2017

  • A new spatial Convolutional Neural Network for graphs and manifolds: MoNet. [PDF]
 

Pubblications

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