Academic Profile

Academic Profile

Assoc Prof Xavier Bresson

NRF Fellow, School of Computer Science and Engineering
Nanyang Associate Professor (NRF)

School of Computer Science and Engineering
College of Engineering

Phone: (+65)6790 6300
Office: N4-02b-40

  • PhD in Science and Technology Swiss Federal Institute of Technology 2005
  • MSc (Electrical Engineering) Ecole Superieure d'electricite 2000
  • MSc (Signal Processing) University of Paris XI 2000
  • BSc (Theoretical Physics) University of Marseille 1998
Xavier Bresson (PhD 2005, EPFL, Switzerland) is Associate Professor in Computer Science and member of the Data Science and AI Research Centre at NTU, Singapore. He is a leading researcher in the field of graph deep learning, a new framework that combines graph theory and deep learning techniques to tackle complex data domains in neuroscience, genetics, social science, physics, and natural language processing. He received in 2016 the highly competitive Singaporean NRF Fellowship of 2.5M US$ to develop these new deep learning techniques. He has also organized international workshops and tutorials with Facebook, NYU, and USI about this emerging field such as the upcoming 2018 UCLA workshop,, the 2017 CVPR tutorial,, and the 2017 NIPS tutorial, He has published more than 60 peer-reviewed papers, including NIPS, ICML, JMLR, the top venues in machine learning. He was awarded several research grants in the U.S. and Hong Kong. He has multiple consulting experiences with e.g. Nestle to design industrial deep learning techniques. On the teaching side, he was the main investigator in 2015 of the first course on deep learning at EPFL, Switzerland, which is now fully part of the EPFL undergraduate and graduate programs. He also designed a praised three-day data training on deep learning and standard techniques for various companies:

For more information:
Research Interests
Data Science
Deep Learning
Artificial Intelligence
Computer Vision
Sparse Convex Optimization
Spectral Graph Theory
Current Projects
  • Spectral Networks and Sparse Representations for Advanced Data Analysis
  • Unifying Spectral Networks and Sparse Representations for Big Data Science

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