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Dr. ir. Floris Laporte 

This person is working in this group since 2015.
Affiliation: Ghent University
Department of Information Technology
Address: Technologiepark-Zwijnaarde 126
9052 Gent
Belgium
Office: iGent, Office 140.010
Phone: +329264346
Fax: +3292643593
E-mail: floris.laporte@ugent.be
Twitter: @florislaporte
ORCID: 0000-0002-4850-4641
ResearchGateID: floris_laporte
LinkedIn: florislaporte
Promotors: Peter Bienstman and Joni Dambre
PhD Thesis: Floris Laporte, Nieuwe architecturen voor brein-geinspireerde fotonische computers, Novel architectures for brain-inspired photonic computers, 3/2020
FlorisLaporte
Floris Laporte was born in Ghent, Belgium, in 1990. He received a joined M.Sc. degree in Photonics from Ghent University, the Free University of Brussels and the University of St. Andrews in 2015. He is currently pursuing a Ph.D. degree in Photonics Engineering at the Photonics Research Group at Ghent University-imec, Belgium. His research focuses on photonic neuromorphic computing and machine learning for photonics.

Specific Research Topics

Patents

Publications (19)

    International Journals

  1. A. Lugnan, A. Katumba, F. Laporte, M. Freiberger, S. Sackesyn, C. Ma, E.J.C. Gooskens, J. Dambre, P. Bienstman, Photonic neuromorphic information processing and reservoir computing, APL Photonics (invited), 5, p.020901 doi:10.1063/1.5129762 (2020)  Download this Publication (2.9MB).
  2. F. Laporte, J. Dambre, P. Bienstman, Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch, Scientific Reports, 9(1), p.5918 doi:10.1038/s41598-019-42408-2 (2019)  Download this Publication (1.3MB).
  3. A. Katumba, M. Freiberger, F. Laporte, A. Lugnan, S. Sackesyn, C. Ma, J. Dambre, P. Bienstman, Neuromorphic computing based on silicon photonics and reservoir computing, IEEE Journal on Selected Topics in Quantum Electronics (invited), 24(6), p.8300310 (10 pages) doi:10.1109/JSTQE.2018.2821843 (2018).
  4. F. Laporte, A. Katumba, J. Dambre, P. Bienstman, Numerical demonstration of neuromorphic computing with photonic crystal cavities, Optics Express, 26(7), p.7955-7964 doi:10.1364/OE.26.007955 (2018)  Download this Publication (8.3MB).
    Book / Book Chapter

  1. A. Katumba, M. Freiberger, F. Laporte, A. Lugnan, S. Sackesyn, C. Ma, J. Dambre, P. Bienstman, Integrated on-chip reservoirs, (invited) publication in Photonic Reservoir Computing: Optical Recurrent Neural Networks ,  (to be published).
    International Conferences

  1. F. Laporte, A. Katumba, M. Freiberger, A. Lugnan, S. Sackesyn, C. Ma, E.J.C. Gooskens, J. Dambre, P. Bienstman, Photonic Reservoir Computing, Photonic Integration Week (invited), Spain, (2020).
  2. P. Bienstman, J. Dambre, A. Katumba, M. Freiberger, F. Laporte, A. Lugnan, S. Sackesyn, C. Ma, E.J.C. Gooskens, Non-linear signal equalisation using silicon photonic reservoir computing, ECOC machine learning workshop (invited), Ireland, (2019).
  3. P. Bienstman, J. Dambre, A. Katumba, M. Freiberger, F. Laporte, A. Lugnan, S. Sackesyn, C. Ma, Neuromorphic information processing using silicon photonics, SPIE Optics and Photonics (invited), United States, p.11081-54 doi:10.1117/12.2524707 (2019)  Download this Publication (254KB).
  4. P. Bienstman, J. Dambre, A. Katumba, M. Freiberger, F. Laporte, A. Lugnan, S. Sackesyn, C. Ma, E.J.C. Gooskens, Silicon photonics reservoir computing at 32 Gbit/s, 5th Workshop on Dynamical Systems and Brain-Inspired Information Processing (invited), Germany, (2019).
  5. F. Laporte, J. Dambre, P. Bienstman, Photontorch: Simulation and Optimization of Large Photonic Circuits Using the Deep Learning Framework PyTorch, IEEE Photonics Society Summer Topicals, United States, p.paper WE1.2 doi:10.1109/phosst.2019.8794941 (2019)  Download this Publication (271KB).
  6. C. Ma, F. Laporte, S. Sackesyn, J.Dambre, P. Bienstman, Optical readout for low resolution weighting and easy observation for integrated photonic reservoir computing, accepted for publication in IEEE Benelux Chapter Annual Symposium 2019,  (to be published).
  7. F. Laporte, J. Dambre, P. Bienstman, Neuromorphic Computing with Signal-Mixing Cavities, IEEE International Conference on Rebooting Computing, United States, doi:10.1109/icrc.2018.8638622 (2018).
  8. P. Bienstman, J. Dambre, A. Katumba, M. Freiberger, F. Laporte, A. Lugnan, Photonic reservoir computing: a brain-inspired approach for information processing, The Optical Fiber Communication Conference (OFC) (invited), United States, p.paper M4F.4 (3 pages) doi:10.1364/OFC.2018.M4F.4 (2018).
  9. P. Bienstman, J. Dambre, A. Katumba, M. Freiberger, F. Laporte, A. Lugnan, Silicon photonics for neuromorphic information processing , SPIE Photonics West (invited), DL 10551, United States, p.paper 10551-19 (7 pages) doi:10.1117/12.2284391 (2018).
  10. F. Laporte, A. Lugnan, J. Dambre, P. Bienstman, Novel photonic reservoir computing architectures, Workshop on Dynamical Systems and Brain-inspired Information Processing, , Germany, (2017)  Download this Publication (372KB).
  11. A. Katumba, F. Laporte, A. Lugnan, J. Dambre, P. Bienstman, Integrated-photonics implementation of reservoir computing neural networks, Machine learning workshop at ECOC (invited), Sweden, (2017).
  12. F. Laporte, J. Dambre, P. Bienstman, Reservoir Computing with signal-mixing cavities, 19th International Conference on Transparent Optical Networks (ICTON) (invited), Spain, p.We.A5.3 doi:10.1109/ICTON.2017.8024990 (2017).
  13. F. Laporte, J. Dambre, P. Bienstman, Header recognition with signal-mixing cavities, Workshop on Dynamical Systems and Brain-inspired Information Processing, Belgium, (2017).
  14. F. Laporte, J. Dambre, P. Bienstman, Photorefractive crystals as brain-inspired photonic reservoir computing systems, IEEE Photonics Society Benelux, Belgium, p.151-154 (2016)  Download this Publication (210KB).
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