Maz Khansari is a machine learning and computer vision scientist with eight years of industry and academic experience, currently driving ML initiatives at Amazon's customer trust division. He has a strong track record translating research into production: leading six ML/CV projects at scale, collaborating with hundreds of scientists, and publishing internally and externally. Previously he pioneered 2D/3D medical imaging methods at USC and UIC, securing NIH funding and contributing to high-impact publications including work cited across the research community. Comfortable with both research and engineering, he builds end-to-end pipelines—from MATLAB/C++ image processing to TensorFlow/Keras models—and has shipped applied solutions such as an emotion-detection web app. Based in San Diego, he blends deep academic rigor (PhD and postdoc) with practical results that increased lab funding and adoption of novel biomarkers.
8 years of coding experience
9 years of employment as a software developer
Postdoc Machine Learning and Computer Vision, Postdoc Machine Learning and Computer Vision at University of Southern California
Bachelor of Science (BS) Electrical and Electronics Engineering, Bachelor of Science (BS) Electrical and Electronics Engineering at Azad University (IAU)
Doctor of Philosophy (PhD) Biomedical Engineering Data science machine learning and medical image processing, Doctor of Philosophy (PhD) Biomedical Engineering Data science machine learning and medical image processing at University of Illinois Chicago
Contributions:1 release, 52 commits, 49 pushes in 1 year 2 months
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Maz Khansari - Machine Learning Applied Scientist Data Scientist