MSc F. Gama

Circuits and Systems (CAS), Department of Microelectronics

Expertise: Graph signal processing

Themes: Signal processing for communication


Fernando Gama is a PhD student at Penn State, and was a visitor at CAS from July until September 2017, working with Geert Leus.


  1. Convolutional neural network architectures for signals supported on graphs
    F. Gama; A.G. Marques; G. Leus; A. Ribeiro;
    IEEE Tr. Signal Processing,
    Volume 67, Issue 4, pp. 1034-1049, February 2019. DOI: 10.1109/TSP.2018.2887403

  2. Aggregation Graph Neural Networks
    F. Gama; A.G. Marques; A. Ribeiro; G. Leus;
    In 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
    Brighton, UK, IEEE, pp. 4943-4947, May 2019. DOI: 10.1109/ICASSP.2019.8682975

  3. Aggregation Convolutional Neural Networks for Graph Signals
    F. Gama; A. Ribeiro; A. Marques; G. Leus;
    In Graph Signal Processing Workshop (GSP18),
    Lausanne (CH), June 2018.

  4. Control of graph signals over random time-varying graphs
    F. Gama; E. Isufi; G. Leus; A. Ribeiro;
    In 2018 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP),
    Calgary (Canada), IEEE, pp. 4169-4173, April 2018. ISSN: 2379-190X. DOI: 10.1109/ICASSP.2018.8462381

  5. Convolutional neural networks via node-varying graph filters
    F. Gama; G. Leus; A. Marques; A. Ribeiro;
    In IEEE Data Science Workshop (DSW18),
    Lausanne (CH), IEEE, pp. 1-5, June 2018. DOI: 10.1109/DSW.2018.8439899

  6. MIMO Graph Filters for Convolutional Neural Networks
    F. Gama; A.G. Marques; A. Ribeiro; G. Leus;
    In Proc. of the IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC 2018),
    Kalamata, Greece, June 2018.

BibTeX support

Last updated: 17 Oct 2017

Fernando Gama