Hameed Abdul
Machine Learning Engineer at Adobe
Champaign, Illinois, United States
Summary
👤
Senior🎓
Top SchoolHameed Abdul is a Machine Learning Engineer and CS PhD candidate at the University of Illinois Urbana-Champaign with nine years of experience building practical ML systems across industry and academia. He currently applies research-grade ML at Adobe and has a strong track record in deep learning, variational latent-space models, and graph neural networks from projects at The University of Southern Mississippi, Brown, and Carnegie Mellon. His contributions to PyTorchZeroToAll show hands-on expertise implementing and optimizing core models and training pipelines for tasks like MNIST classification and medical prediction. He has also led large-data efforts—building a comprehensive scene/sketch/3D dataset and organizing a 3D retrieval challenge—demonstrating both engineering scale and community leadership. Notably, his background spans web and visualization tooling to pipeline productionization, enabling him to bridge research prototypes to deployable systems.
9 years of coding experience
1 year of employment as a software developer
Bachelor of Applied Science (B.A.Sc.), Computer Science, Bachelor of Applied Science (B.A.Sc.), Computer Science at The University of Southern MississippiUniversity of Illinois Urbana-Champaign
English, Korean
Github Skills (10)
pytorch10
deep-learning10
mnist10
linear-regression9
python9
logistic-regression9
mask-rcnn9
faster-rcnn9
user-manual8
basics8
Programming languages (6)
Github contributions (5)
hunkim/PyTorchZeroToAll
Jun 2019 - Oct 2019
Simple PyTorch Tutorials Zero to ALL!
Role in this project:
ML Engineer
ML EngineerContributions:12 commits, 1 PR, 1 comment in 4 months
Contributions summary:Hameed's commits primarily focus on building and refining PyTorch-based deep learning models. The user demonstrates hands-on experience with linear regression, logistic regression, and convolutional neural networks for tasks like MNIST classification and diabetes prediction. Contributions involve implementing model architectures, training loops, loss functions, and optimization strategies. The user also refactors code for better structure and efficiency.
pytorchpythonzerodeep-learningpytorch-tutorials
Hammania689/linux_environment
Nov 2018 - Apr 2023
Contributions:61 pushes, 1 branch in 4 years 6 months
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