Wei-chih Chern is a Ph.D. student and computer vision engineer with eight years of experience applying ML and deep learning to real-world systems, currently researching action localization and weakly-supervised segmentation for construction safety at the University of Dayton. He brings practical embedded and production experience—from accelerating AR and ADAS pipelines and optimizing models for TI hardware to deploying vision stacks with Docker and Redis—bridging research and applied engineering. His background includes SAR-based glacier detection and face recognition, demonstrating skill with diverse sensor modalities and domain transfer. As a former team co-lead in industry, he has delivered performance gains through algorithmic and systems optimizations (FPS and mAP improvements) and model quantization for edge devices. Advisored jointly by labs in the U.S. and South Korea, he combines collaborative academic rigor with hands-on product deployment.
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