Soroosh Baselizadeh is a Machine Learning Engineer with nine years of experience and a Master’s in Computer Science from the University of Waterloo, currently applying deep learning expertise at Electronic Arts. He has a strong research-to-production track record—improving SOTA performance 2–20% across segmentation, anomaly detection, and interpretable ML—and has co-authored work accepted at venues like CVPR, ICML, and MICCAI. His background spans building novel architectures (joint segmentation/boundary models, edge upsampling), explainability methods (critical pathways, sanity checks), and practical pipelines for dataset extension and ground-truth generation. Comfortable across PyTorch/Keras, Python data stacks, Git, and Linux servers, he pairs rigorous experiments with reproducible engineering. Notably, he translated research insights into industrial gains (e.g., a 20% anomaly detection boost on medical/industrial sets) and has experience collaborating with international teams at Oxford and TUM. Based in Vancouver, he blends publication-driven research discipline with product-focused implementation and strong communication skills.
9 years of coding experience
4 years of employment as a software developer
Bachelor's degree, Computer Engineering, Bachelor's degree, Computer Engineering at Sharif University of Technology
Master's degree, Computer Science, Master's degree, Computer Science at University of Waterloo
High School Diploma, Mathematics and Physics, High School Diploma, Mathematics and Physics at National Organization for Development of Exceptional Talents (Sampad)
Contributions:116 pushes, 1 branch in 4 years 4 months
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Soroosh Baselizadeh - Machine Learning Engineer at Electronic Arts (EA)