Hager Abdelwahed
Senior Applied Research Scientist at Mila - Institut québécois d'intelligence artificielle
Canada
Summary
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Senior🎓
Top SchoolHager Abdelwahed is a Senior Applied Research Scientist at Mila with 11 years of experience applying RL, computer vision and deep learning to real-world problems, particularly in healthcare and autonomy. She holds an MSc in Computer Science from the University of Alberta (4.0 GPA) where her research focused on offline and safe reinforcement learning and RL with human feedback. At Mila she bridges academic and applied AI, building LLMs, AI agents and vision models while advising startups through NextAI; earlier roles include perception work for autonomous driving and industry research at Affectiva. An active practitioner in PyTorch, she has contributed to reusable project templates and GAN/agent implementations, reflecting both robust engineering and reproducible research practices. Colleagues describe her as mission-driven toward social good and skilled at translating rigorous research into deployable ML systems.
11 years of coding experience
6 years of employment as a software developer
Master of Science - MSc, Computer Science, 4.0/4.0, Master of Science - MSc, Computer Science, 4.0/4.0 at University of AlbertaBachelor of Science - BSc, Computer Engineering, 3.67 / 4.0 , high honors, Bachelor of Science - BSc, Computer Engineering, 3.67 / 4.0 , high honors at The American University in Cairo
English, Arabic
Github Skills (9)
pytorch10
machine-learning10
deep-learning10
cgan10
cyclegan10
python10
dcgan10
image-processing8
tensorboard7
Programming languages (4)
Github contributions (5)
moemen95/Pytorch-Project-Template
Jun 2018 - Jul 2018
A scalable template for PyTorch projects, with examples in Image Segmentation, Object classification, GANs and Reinforcement Learning.
Role in this project:
ML Engineer
ML EngineerContributions:26 commits in 1 month
Contributions summary:Hager contributed significantly to the project by implementing various components of a DCGAN model, including the generator, discriminator, and loss functions. They also integrated the CelebA dataset for training. Furthermore, the user has experience with the MNIST dataset and implemented the basic agent structure. The commits demonstrate the user's understanding of PyTorch and its related libraries for deep learning.
image-segmentationpytorch-project-templatescemanticreinforcementclassification
hagerrady13/hagerrady13.github.io
Jan 2020 - Apr 2024
Contributions:46 pushes, 2 branches in 4 years 3 months
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