Ahmed Gad is a Senior Software Engineer (AI) based in Ottawa with eight years of experience building and deploying machine learning and cloud-native systems. He holds an M.Sc. in Information Technology and has authored five technical books, seven research papers, and a popular open-source Python optimization library, PyGAD, used for genetic-algorithm-based ML tuning. Ahmed has designed and productionized secure AWS platforms for healthcare, built cancer-detection models that exceeded 98% accuracy, and automated CI/CD and IaC workflows to fully containerize deployments. His work spans from low-level implementations—like NumPy-built CNNs and optimized mutation operators—to adapting complex models such as Mask R-CNN for TensorFlow 2.0 and on-device ML for mobile. Known for clear technical writing and reproducible engineering, he combines academic rigor with practical system building and a knack for turning research prototypes into reliable, deployable software.
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow 2.0
Role in this project:
ML Engineer
Contributions:3 releases, 39 commits, 44 pushes in 1 year 8 months
Contributions summary:Ahmed primarily focused on adapting the Mask R-CNN model for TensorFlow 2.0, as indicated by the initial commit. This included significant code modifications to support the new TensorFlow version. Furthermore, the user added code for a transfer learning example using a kangaroo dataset, demonstrating expertise in customizing the model and training on a specific dataset. They also contributed scripts for prediction and made minor adjustments to improve overall functionality.
Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
Role in this project:
ML Engineer
Contributions:36 releases, 15 reviews, 186 commits in 3 years 11 months
Contributions summary:Ahmed primarily contributed to the development and modification of a genetic algorithm library for machine learning applications. They implemented core genetic algorithm functions for the evolution and training of solutions. Their work includes the creation of an example using a genetic algorithm to optimize model parameters of a model. Furthermore, the user made improvements to the mutation operation of the genetic algorithm.
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