Alexia Jolicoeur-martineau
Montreal, Quebec, Canada
Summary
🤩
Rockstar🎓
Top SchoolAlexia Jolicoeur-martineau is a senior AI researcher with a decade of experience building generative models across images, video, text, tabular data, neural network weights, molecules, and video games at Samsung SAIT AI Lab Montréal. She blends academic rigor from Université de Montréal with hands-on engineering, routinely implementing and tuning GAN variants (DCGAN, WGAN, LSGAN) and production-oriented tooling like TensorBoard and PyTorch data pipelines. Her portfolio includes playful yet technically solid open-source work such as a DCGAN for cat image generation, reflecting both deep research interests and practical experimentation. Based in Montréal, she excels at translating cutting-edge generative research into reproducible code and scalable prototypes. Colleagues value her for combining broad modality coverage with attention to training stability and monitoring, a skillset that bridges research and product needs.
10 years of coding experience
Université de Montréal
French, English
Github Skills (14)
data-preprocessing10
data-loading10
pytorch10
machine-learning10
computer-vision10
dataprep10
deep-learning10
preprocessing10
python10
load-data10
generative-adversarial-network10
preprocess10
image-processing9
tensorboard9
Programming languages (5)
Github contributions (5)
AlexiaJM/Deep-learning-with-cats
Jun 2017 - Jul 2019
Deep learning with cats (^._.^)
Role in this project:
ML Engineer
ML EngineerContributions:97 commits, 6 PRs, 96 pushes in 2 years 1 month
Contributions summary:Alexia primarily contributed to the development of a DCGAN model for generating cat images. Their work involved setting up the model architecture, including the generator and discriminator networks, configuring hyperparameters, and defining the training loop. They also integrated tensorboard for monitoring the training progress and implemented data loading and preprocessing steps using the PyTorch framework. The user further experimented with WGAN and LSGAN models to improve results.
deep-learningcudaganpicture
AlexiaJM/Generative-model-most-similar-images
Jul 2017 - Aug 2021
Function that output the 5 most similar training (real) images to the given generated (fake) images.
Contributions:8 commits, 1 PR, 7 pushes in 4 years 1 month
similarmachine-learningtrainingimage-processingfake-images
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