Hossam Mohammed is a Senior Machine Learning Engineer and PhD candidate in Transportation Engineering at UBC with eight years of experience building production-grade AI systems for telecom, finance, embedded devices, and transportation research. He architects end-to-end MLOps and personalization platforms—bringing Databricks, Snowflake, MLflow, and CI/CD together—to run millions of adaptive decisions weekly and cut manual model review cycles by 40%. His background in transportation and imitation learning informs practical RL and contextual bandit solutions that delivered 30% campaign uplifts, while his embedded work optimized on-device models for 3× faster inference with no accuracy loss. A proven bridge between research and product, he open-sources research code, teaches machine learning, and uniquely combines deep domain knowledge in mobility with hands-on engineering to deploy scalable, observable AI in constrained and cloud environments.
8 years of coding experience
9 years of employment as a software developer
Doctor of Philosophy (PhD), Transportation Engineering, Doctor of Philosophy (PhD), Transportation Engineering at The University of British Columbia
Bachelor's degree, Civil Engineering, Distinguished (Hons), Bachelor's degree, Civil Engineering, Distinguished (Hons) at Cairo University
Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE
Contributions:19 pushes in 26 days
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Hossam Mohammed - Senior Machine Learning Engineer at Symend