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Julian Bueno


  • United Kingdom

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Personal profile

Personal Statement

My research focuses on building neural networks with photonic and optical hardware for ultrafast and efficient information porcessing. I have worked with different systems and concepts including:

  • Photonic neurons: edge emitter laser, VCSELs, and even Spatial Light Modulators.
  • Kinds of Neurons: excitable/spiking and chaotic.
  • Architectures: Feed Forward Networks, Recurrent Neural Networks, Reservoirs, and Time Delay Reservoirs.
  • Trainings: Supervised Learning, and Reinforced Learning.
  • Applications: prediction (e.g. prediciton of chaotic timeseries) and classification tasks (e.g. equalizators, nonlinear compensation for improved SNR...).
  • Optical systems in fiber based and free space optics.

Each feature has its own advantages and challenges, impacting in the overall device specifications and performance. I study their properties and capabilities, what they can do and why they can do it, because I want to design and build the most efficient and capable neural networks, operating at the speed of light, and harnessing the computational power of nonlinear systems.

I am eager to include more hardware and ideas into the mix. Feel free to contact me if you have some idea that could help us both.


  • Semiconductor Lasers
  • Nonlinear dynamics
  • Photonics
  • Neural Networks
  • Reservoir Computing
  • Lasers
  • Nonlinear Photonics

Fingerprint Dive into the research topics where Julian Bueno is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

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Fibers Chemical Compounds
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Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Research Output 2016 2020

  • 5 Article
  • 4 Conference contribution book
  • 1 Chapter

Large scale spatiotemporal reservoirs

Brunner, D., Bueno, J., Porte, X., Maktoobi, S. & Andreoli, L., 8 Jul 2019, Photonic Reservoir Computing: Optical Recurrent Neural Networks. Brunner, D., Soriano, M. C. & Van der Sande, G. (eds.). Berlin, p. 83-116 34 p.

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)
11 Downloads (Pure)

PAM-4 transmission at 1550 nm using photonic reservoir computing post-processing

Argyris, A., Bueno, J. & Fischer, I., 19 Mar 2019, In : IEEE Access. 7, p. 37017 - 37025 9 p., 8669764.

Research output: Contribution to journalArticle

Open Access
Pulse amplitude modulation
Signal to noise ratio