Mazid Osseni
Conseiller En Intelligence Artificielle Et Valorisation Des Données
Quebec, Quebec, Canada
Summary
👤
Senior🎓
Top SchoolMazid Osseni is a Machine Learning researcher and PhD candidate at Université Laval with 10 years of experience applying interpretable, parsimonious models to healthcare and bioinformatics problems. Currently advising on AI and data valorization at the Institut universitaire de cardiologie et de pneumologie de Québec, he blends research leadership with hands-on Python development and a track record of conference publications. He is skilled in both supervised and unsupervised methods, with a preference for transparent techniques (set covering machines, decision trees, random forests) and a special interest in natural language processing. His background includes industry-facing roles at Leap Conseil and Valence Discovery and long-standing academic teaching and research positions, giving him a rare combination of applied impact and rigorous scientific grounding. Colleagues value his intellectual curiosity and ability to communicate complex predictive concepts clearly to multidisciplinary teams.
11 years of coding experience
8 years of employment as a software developer
Ph.D Candidate in Machine Learning, Ph.D Candidate in Machine Learning at Université Laval
French, English
Github Skills (12)
regularization9
pytorch8
elastic-net8
python7
algorithms7
machine-learning6
dynamic-programming6
genomics3
bioinformatics3
core-library2
genome-analysis2
graph1
Programming languages (2)
Github contributions (5)
dizam92/pyTorchReg
Feb 2019 - May 2022
Applied Sparse regularization (L1), Weight decay regularization (L2), ElasticNet, GroupLasso and GroupSparseLasso to Neuronal Network.
Contributions:12 commits, 2 PRs, 7 pushes in 3 years 3 months
elastic-netregularizationregularization-methodspytorch
dizam92/multiomic_predictions
Mar 2021 - Sep 2022
Contributions:41 commits, 3 PRs, 54 pushes in 1 year 6 months
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