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Dr. Alessio Lugnan  (Postdoctoral Researcher)

This person worked in the group from 2016 till 2023.

Affiliation: Ghent University - IMEC
Department of Information Technology
Address: Technologiepark-Zwijnaarde 126
9052 Ghent
Office: iGent, Office 140.012
Phone: +32 492062776
E-mail: Alessio.Lugnan@UGent.be
ORCID: https://orcid.org/0000-0002-6587-2614
Research Area: Neuromorphic computing, photonics, machine learning
Promotors: Peter Bienstman and Joni Dambre
PhD Thesis: Alessio Lugnan, Op fotonica gebaseerd machinaal leren om labelvrije flowcytometrie te versnellen en te vereenvoudigen , Photonics-Based Machine Learning to Speed up and Simplify Label-Free Flow Cytometry , 9/2021
AlessioLugnan
Alessio Lugnan received a Master's degree in Experimental Physics from the University of Trento in 2016. He joined the Photonics Research Group (Ghent University / Imec) in 2016 as a PhD student, working on optical solutions for machine learning classification of particles for imaging microflow cytometry and on integrated photonic reservoir computing using ring resonators. He received a PhD in Photonics Engineering in 2021 and is currently working as a postdoctoral researcher in the same group, on neuromorphic computing with silicon photonics and phase change materials.

Specific Research Topics

Current PhD Students

Patents

Publications (-1)

    International Journals

  1. M. Gouda, A. Lugnan, J. Dambre, G. V. Branden, C. Posch, P. Bienstman, Improving the classification accuracy in label-free flow cytometry using event-based vision and simple logistic regression, IEEE Journal on Selected Topics in Quantum Electronics, (Optical computing), p.8 doi:10.1109/JSTQE.2023.3244040 (2023)  Download this Publication (4.8MB).
  2. A. Lugnan, Santiago Garcia-Cuevas Carrillo, C. David Wright, P. Bienstman, Rigorous dynamic model of a silicon ring resonator with phase change material for a neuromorphic node, Optics Express, 30(14), p.25177-25194 doi:10.1364/OE.459364 (2022)  Download this Publication (2.6MB).
  3. Santiago Garcia-Cuevas Carrillo, A. Lugnan, Emanuele Gemo, P. Bienstman, Wolfram H. P. Pernice, Harish Bhaskaran, C. David Wright, System-Level Simulation for Integrated Phase-Change Photonics, Journal of lightwave technology, 39(20), p. 6392 - 6402 doi:10.1109/JLT.2021.3099914 (2021)  Download this Publication (3MB).
  4. A. Lugnan, E.J.C. Gooskens, J. Vatin, J. Dambre, P. Bienstman, Machine learning issues and opportunities in ultrafast particle classification for label‑free microflow cytometry, Scientific Reports, 10(1), p.1-13 doi:10.1038/s41598-020-77765-w (2020)  Download this Publication (2MB).
  5. 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).
  6. 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).
  7. A. Lugnan, J. Dambre, P. Bienstman, Integrated pillar scatterers for speeding up classification of cell holograms, Optics Express, 25(24), p.30526-30538 doi:10.1364/oe.25.030526 (2017)  Download this Publication (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, Photonic Reservoir Computing: Optical Recurrent Neural Networks (invited), p.53-82 (2019).
    International Conferences

  1. A. Lugnan, A. Foradori, S. Biasi, P. Bienstman, L. Pavesi, Large-scale neural networks with passive silicon photonics for biologically plausible learning, Photonics Europe, France, p.13017-50 (2024)  Download this Publication (894KB).
  2. P. Bienstman, A. Lugnan, S. Aggarwal, F. Brückerhoff-Plückelmann, W. Pernice, H. Bhaskaran, C. Ma, S. Sackesyn, E.J.C. Gooskens, S. Masaad, M. Gouda, R. De Prins, Optical computing in silicon photonics: self-adapting ring networks and quantum recurrent neural networks, Natural and Physical Computing (NNPC), Germany, p.1 (2023)  Download this Publication (207KB).
  3. S. Masaad, E.J.C. Gooskens, M. Gouda, R. De Prins, F. Marchesin, R. Shi, A. Lugnan, S. Sackesyn, C. Ma, Joni Dambre, P. Bienstman, Integrated Photonic Reservoir Computing for Telecommunication Applications , Neuromorphic photonics and applications (invited), Greece, (2023).
  4. A. Lugnan, S. Garcia-Cuevas Carillo, J. Song, S. Aggarwal, F. Brückerhoff-Plückelmann, W. Pernice, H. Bhaskaran, D. Wright, P. Bienstman, Silicon ring resonator with phase-change material as a plastic dynamical node for scalable all-optical neural networks with synaptic plasticity, ICTON, Romania, p.4 (2023)  Download this Publication (361KB).
  5. Steven Abreu, M. Gouda, A. Lugnan, P. Bienstman, Flow cytometry with event-based vision and spiking neuromorphic hardware, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Canada, doi:10.1109/CVPRW59228.2023.00435 (2023)  Download this Publication (2.7MB).
  6. A. Lugnan, S. Aggarwal, F. Brückerhoff-Plückelmann, W. Pernice, H. Bhaskharan, P. Bienstman, Performance enhancement via synaptic plasticity in an integrated photonic recurrent neural network with phase-change materials, CLEO-EQEC Europe, Germany, p.JSIII-3.5 (2023)  Download this Publication (352KB).
  7. M. Gouda, A. Lugnan, J. Dambre, G. V. Branden, C. Posch, P. Bienstman, Event-based vision for improved classification accuracy in label-free flow cytometry, IEEE Benelux Photonics Chapter - Annual Symposium 2022, Netherlands, p.26-29 (2022)  Download this Publication (2.2MB).
  8. P. Bienstman, A. Lugnan, C. Ma, S. Sackesyn, E.J.C. Gooskens, S. Masaad, M. Gouda, R. De Prins, Coherent optical computing in silicon photonics, Coherent Network Computing (CNC), , United States, p.D2.12.00 (2022)  Download this Publication (71KB).
  9. P. Bienstman, J. Dambre, A. Lugnan, S. Sackesyn, C. Ma, E.J.C. Gooskens, M. Gouda, S. Masaad, Photonic Neuromorphic Computing Using Silicon Chips, Huawei STW (invited), (2021).
  10. A. Lugnan, S. Sackesyn, C. Ma, E.J.C. Gooskens, M. Gouda, S. Masaad, J. Dambre, P. Bienstman, Reservoir computing for high-speed photonic information processing, Photonics in Switching and Computing (invited), p.TuA2.3 (2021)  Download this Publication (2MB).
  11. A. Lugnan, S. Sackesyn, C. Ma, E.J.C. Gooskens, M. Gouda, S. Masaad, Joni Dambre, P. Bienstman, Photonic reservoir computing for high-speed neuromorphic computing applications, 2021 IEEE Summer Topicals Meeting Series (invited), Mexico, (2021).
  12. P. Bienstman, J. Dambre, A. Lugnan, S. Sackesyn, C. Ma, E.J.C. Gooskens, M. Gouda, S. Masaad, Silicon photonics for brain-inspired neuromorphic information processing, 1st Workshop on Neuromorphic Photonics (invited), (2020)  Download this Publication (4.3MB).
  13. 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).
  14. 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).
  15. 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).
  16. 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).
  17. A. Lugnan, J. Dambre, P. Bienstman, Numerical investigation of integrated dielectric pillars to simplify machine learning classification of cells, 23rd Annual Symposium of the IEEE Photonics Benelux Chapter, Belgium, (2018)  Download this Publication (524KB).
  18. A. Lugnan, J. Dambre, P. Bienstman, Integrated dielectric scatterers for speeding up classification of cell diffraction patterns, 2018 20th International Conference on Transparent Optical Networks (ICTON) (invited), Romania, p.We.A6.3, 4pp. doi:10.1109/ICTON.2018.8473611 (2018)  Download this Publication (771KB).
  19. A. Lugnan, J. Dambre, P. Bienstman, Integrated dielectric scatterers for fast optical classification of biological cells, SPIE Photonics Europe, 10689(07), France, p.1-7 doi:10.1117/12.2306654 (2018)  Download this Publication (636KB).
  20. 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).
  21. 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).
  22. 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).
  23. 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).
  24. A. Lugnan, J. Dambre, P. Bienstman, Integrated pillar scatterers for speeding up classification of cell holograms through a RC-like machine learning approach, Workshop on Dynamical Systems and Brain-inspired Information Processing, Belgium, (2017)  Download this Publication (556KB).
      National Conferences

    1. A. Foradori, A. Lugnan, L. Pavesi, P. Bienstman, Reservoir computing with a silicon microring resonator matrix for image classification, ICOP 2024, Italy, p.1 (2024)  Download this Publication (108KB).
    2. P. Bienstman, J. Dambre, A. Lugnan, S. Sackesyn, C. Ma, E.J.C. Gooskens, M. Gouda, S. Masaad, Photonic Neuromorphic Computing Using Silicon Chips, BePOM (invited), (2021)  Download this Publication (4.4MB).
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