Resume

Education

• [Expected] Ph.D in Computer Science, University of Lille (France), 2025
• M.S. in Computer Science, University Lyon 1 (France), 2022
  • Specialization in artificial intelligence
  • 1st year rank: 10/121
  • 2nd year rank: 4/31
• B.S. in Computer Science, University Lyon 1 (France), 2020
  • One-year exchange at Tecnológico de Monterrey (Mexico), 2019

Work experience

• Research internship (2022, 6 months)
  • Location: Institut interdisciplinaire d’innovation technologique, Sherbrooke, Canada
  • Tasks: Acoustic anomaly detection using spiking neural networks for low-power IoT devices
  • Wrote a scientific paper
• Full-stack developer internship (2020, 2 months)
  • Location: University Lyon 3, Lyon, France
  • Tasks: Web application with .NET and Angular

Certifications

• Course “Linear Algebra for Machine Learning and Data Science”, DeepLearning.AI, 2023 (credential)
• Scientific mediator, University of Lille, 2023 (credential)
• Course “Sharing knowledge with Wikipedia”, University of Lille, 2023
• Course “Latex, advanced level”, University of Lille, 2023
• Course “Effective reading of scientific papers”, University of Lille, 2022
• Course “Computational Neuroscience”, University of Washington, 2022 (credential)
• Specialization “Deep Learning”, DeepLearning.AI, 2021 (credential)

Skills

• Spiking neural networks
• Neuromorphic computing
• Bio-Inspired Computing
• Computer vision
• Deep learning
• Reinforcement learning
• Machine learning frameworks
  • Pytorch
  • Tensorflow
  • Scikit-Learn
• Programming languages
  • Python
  • C/C++
  • C#
  • Java
  • Javascript
• Others
  • Unity
  • Git
  • Linux

Publications

Neuronal Competition Groups with Supervised STDP for Spike-Based Classification

Paired competing neurons improving STDP supervised local learning in spiking neural networks

Gaspard Goupy, Pierre Tirilly, and Ioan Marius Bilasco. Paired Competing Neurons Improving STDP Supervised Local Learning in Spiking Neural Networks. Frontiers in Neuroscience, 18, 2024.

Unsupervised and efficient learning in sparsely activated convolutional spiking neural networks enabled by voltage-dependent synaptic plasticity

Gaspard Goupy, Alexandre Juneau-Fecteau, Nikhil Garg, Ismael Balafrej, Fabien Alibart, Luc Frechette, Dominique Drouin, and Yann Beilliard. Unsupervised and Efficient Learning in Sparsely Activated Convolutional Spiking Neural Networks Enabled by Voltage-Dependent Synaptic Plasticity. Neuromorphic Computing and Engineering, 3, 2023.

Others

• Wikipedia contributor