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Davide Boscaini

Researcher
  • Phone: +39 0461 314525
  • FBK Povo
Short bio

Davide Boscaini was born in Verona (Italy) in 1988. He received a B.S. degree in Applied Mathematics in 2010 and a M.S. degree in Mathematics in 2012, both from the University of Verona (Italy). His master thesis on "Spectral Methods for Shape Analysis" was developed during an intership at the Vision, Image, Processing and Sound Lab on topics related with the analysis of 3D deformable objects. The desire to deepen his knowledge in this topic led Davide to start a Ph.D. at the University of Lugano (Switzerland) under the supervision of prof. Michael M. Bronstein. During the Ph.D. his research focussed on merging spectral approaches and deep learning techniques to develop novel algorithms for 3D Shape Analysis. Davide's efforts contributed to the birth of a new research field, called Geometric Deep Learning, aiming at extending deep learning techniques to geometric domains such as 3D shapes and graphs. He received his Ph.D. degree in Computer Science in 2017 with a dissertation on "Geometric Deep Learning for Shape Analysis". He is currently a researcher at the Technologies of Vision research unit of the Fondazione Bruno Kessler in Trento (Italy).

Research interests
3D shape analysis geometric deep learning
Related projects
Publications
  1. Gainza, P.; Sverrisson, F.; Monti, F.; Rodolà, E.; Boscaini, D.; Bronstein, M. M.; Correia, B. E.,
    in «NATURE METHODS»,
    2019
  2. Osterno Vasconcelos, Levi; Mancini, Massimiliano; Boscaini, Davide; Caputo, Barbara; Ricci, Elisa,
    Proceedings of 2019 International Conference on 3D Vision (3DV),
    2019
    , pp. 57-
    66
    , (2019 International Conference on 3D Vision (3DV),
    Québec City, QC, Canada,
    16-19 September 2019)
  3. Boscaini, Davide; Poiesi, Fabio,
    Proceedings of Image Analysis and Processing – ICIAP 2019,
    vol.11751,
    2019
    , pp. 454-
    465
    , (International Conference on Image Analysis and Processing (ICIAP 2019),
    Trento, Italy,
    9-13 September 2019)
  4. Gainza, P.; Sverrisson, F.; Monti, F.; Rodolà, E.; Boscaini, D.; Bronstein, M. M.; Correira, B. E.,
    Learning interaction patterns from surface representations of protein structure,
    Workshop on Graph Representation Learning at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019),
    2019
  5. Monti, Federico; Boscaini, Davide; Masci, Jonathan; Rodolà, Emanuele; Svoboda, Jan; Bronstein, Michael M.,
    Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
    2017
    , pp. 5115-
    5124
    , (IEEE Conference on Computer Vision and Pattern Recognition (CVPR),
    Honolulu, Hawaii,
    21-26 July 2017)
  6. Boscaini, Davide; Masci, Jonathan; Rodolà, Emanuele; Bronstein, Michael M.; Cremers, Daniel,
    Anisotropic diffusion descriptors,
    in «COMPUTER GRAPHICS FORUM»,
    vol. 35,
    n. 2,
    2016
    , pp. 431 -
    441
  7. Boscaini, Davide; Masci, Jonathan; Rodolà, Emanuele; Bronstein, Michael M.,
    Learning shape correspondence with anisotropic convolutional neural networks,
    Proceedings of the 29th Conference on Neural Information Processing Systems (NIPS 2016),
    2016
    , pp. 3189-
    3197
    , (29th Conference on Neural Information Processing Systems (NIPS 2016),
    Barcelona, Spain,
    05/10-12-2016)
  8. Masci, Jonathan; Rodolà, Emanuele; Boscaini, Davide; Bronstein, Michael M.; Li, Hao,
    Geometric deep learning,
    SA '16 SIGGRAPH ASIA 2016 Courses,
    2016
    , (SA '16 SIGGRAPH ASIA 2016 Courses,
    Macao,
    5-8/12/2016)
  9. Boscaini, Davide; Eynard, Davide; Kourounis, Drosos; Bronstein, Michael M.,
    Shape-from-Operator: recovering shapes from intrinsic operators,
    in «COMPUTER GRAPHICS FORUM»,
    vol. 34,
    n. 2,
    2015
    , pp. 265 -
    274
  10. Boscaini, Davide; Masci, Jonathan; Melzi, Simone; Bronstein, Michael M.; Castellani, Umberto; Vandergheynst, Pierre,
    Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks,
    in «COMPUTER GRAPHICS FORUM»,
    vol. 34,
    n. 5,
    2015
    , pp. 13 -
    23

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