Nishat Khan is a Ph.D. student and research assistant in the GRAIL lab at the University of Washington with nine years of experience applying machine learning and computer vision to real-world problems. His work focuses on designing interpretable models and tools with clinical applicability, combining strong academic training (B.Sc. EEE with a 3.87 GPA, MS ECE) and hands-on systems experience. He has built mobile computer-vision systems—most notably a smartphone-based traffic light and sign detector that used sensor fusion and neural classifiers to achieve >95% accuracy and an order-of-magnitude faster processing time. Prior roles include teaching and lecturing in electrical engineering topics, reflecting both technical depth and clear communication skills. Based in Bellevue, WA, he codes across platforms (Android, hobbyist game projects) and brings a practical engineering mindset to research problems. Colleagues would describe him as an applied researcher who bridges algorithmic innovation with deployable mobile solutions.
9 years of coding experience
2 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Washington
Higher Secondary School Certificate, Science, 5.00 out of 5.00, Higher Secondary School Certificate, Science, 5.00 out of 5.00 at B.C.I.C College
Secondary School Certificate, Science, 5.00 out of 5.00, Secondary School Certificate, Science, 5.00 out of 5.00 at Radio Colony Model School, Savar, Dhaka
Bachelor of Science (B.Sc.), Electrical and Electronics Engineering, 3.87 out of 4.00, Bachelor of Science (B.Sc.), Electrical and Electronics Engineering, 3.87 out of 4.00 at Bangladesh University of Engineering and Technology
Master of Science - MS, Electrical and Computer Engineering, Master of Science - MS, Electrical and Computer Engineering at University of Illinois at Chicago
Contributions:2 pushes, 22 branches in 2 years 3 months
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Nishat Khan - Research Assistant at University of Washington