Summary
Zafar Rafii is a research scientist with 8 years of industry experience specializing in audio analysis, machine listening, and lightweight neural networks, currently advancing audio recognition and version identification at Audible Magic. He holds a Ph.D. in Electrical Engineering and Computer Science from Northwestern and has a long track record at Gracenote leading projects in audio fingerprinting, live/cover song identification, spatial audio separation, and audio watermarking. Zafar combines deep academic training with hands-on production experience deploying robust audio classification and detection systems at scale. He has moved from Ph.D. intern to research engineering manager roles, showing a rare blend of technical depth and team leadership. Notably, his work spans both classic signal-processing approaches and modern neural models, optimizing for real-time and resource-constrained environments. Based in the Greater Seattle Area, he maintains an active public profile and portfolio that bridges research and product-focused engineering.
8 years of coding experience
9 years of employment as a software developer
Master’s Degree, Electrical Engineering, Computer Science and Telecommunications, Master’s Degree, Electrical Engineering, Computer Science and Telecommunications at Ecole nationale supérieure de l'Electronique et de ses Applications
Doctor of Philosophy (Ph.D.), Electrical Engineering and Computer Science, Doctor of Philosophy (Ph.D.), Electrical Engineering and Computer Science at Northwestern University
Master’s Degree, Electrical Engineering, Master’s Degree, Electrical Engineering at Illinois Institute of Technology
English, French, German, Italian, Persian, Vietnamese