Alexia Jolicoeur-martineau
Montreal, Quebec, Canada
Summary
🤩
Rockstar🎓
Top SchoolAlexia Jolicoeur-martineau is a senior research scientist in AI based in Montreal with nine years of experience, currently a Senior Researcher and team lead at Samsung SAIT AI Lab. She develops generative models across images, video, text, tabular data, neural network weights, molecules and video games, with deep expertise in GANs, diffusion models and autoregressive transformers. Originally trained as a biostatistician, she published widely in psychology and statistics and mentored MSc/PhD/postdocs before pivoting to AI in 2018 and earning a PhD at Université de Montréal. Her independently written Relativistic GAN paper—trained overnight on a single GTX 1060—stabilized GAN training, achieved state-of-the-art results and has amassed over 1,000 citations. At Samsung she leads autonomous driving research and a company-recognized molecular generation project, while remaining hands-on in open source (e.g., a DCGAN "Deep-learning-with-cats" repo) that blends rigorous research with practical engineering.
9 years of coding experience
Université de Montréal
French, English
Github Skills (18)
pytorch10
preprocessing10
python10
load-data10
preprocess10
datapreprocessing10
pre-processing10
data-prep10
machine-learning10
data-preprocessing10
data-loading10
deep-learning10
data-pre-processing10
computer-vision10
generative-adversarial-networks10
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-learningpytorchmachine-learningtensorflow
AlexiaJM/LEGIT_SAS
Feb 2017 - May 2017
Contributions:17 commits, 15 pushes, 1 branch in 2 months
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