Waleed Khamies is an applied scientist with nine years of experience building and deploying ML systems that automate business processes, most recently focusing on scalable AI-powered scheduling and manufacturing solutions. He blends academic rigor—PhD-level work in AI & Healthcare and research roles at Mila and Brown—with hands-on engineering in production stacks like PyTorch, Kubeflow, Vertex AI, and BigQuery. As an R&D practitioner at NTWIST and a consultant for WesTower, he takes research ideas into industrial workflows using self-supervised and reinforcement learning. He also co-runs GradCorner to demystify AI concepts, reflecting a knack for translating complex methods into accessible guidance. Based in Edmonton, he pairs a strong theoretical background with practical delivery across startups and research labs, often tackling noisy-label and robustness challenges in deep learning.
9 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD AI & Healthcare, Doctor of Philosophy - PhD AI & Healthcare at University of Alberta
MSc Mathematical Sciences – Machine Intelligence, MSc Mathematical Sciences – Machine Intelligence at AIMS - Next Einstein Initiative
Bachelor of Science - BSc Software Engineering of Electronic Systems - Electronics & Computer Engineering, Bachelor of Science - BSc Software Engineering of Electronic Systems - Electronics & Computer Engineering at University of Khartoum
A pytorch Implementation of VAE-based musical model to generate and interpolate piano'notes using Nottingham dataset.
Contributions:16 commits, 4 PRs, 11 pushes in 1 month
pytorchsource-separationnotesinterpolatelstm
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