Fu-chen Chen is an Applied Scientist with 8 years of experience building production-grade computer vision and deep learning systems, currently developing visual perception for Amazon Astro robotics at Lab126. He holds a Ph.D. in Electrical and Computer Engineering from Purdue and has shipped real-time people-tracking and segmentation models at Facebook for AR on mobile devices. His research background includes novel statistical frameworks for detecting tiny cracks in industrial video and combining engineered features with CNNs for robust recognition across varied data quality. Earlier roles span embedded vision and ML at PixArt, where he delivered highly optimized face and icon recognition IC algorithms and power/area improvements, showing strength in translating research into hardware-constrained products. Known for tackling difficult real-world detection problems (e.g., sub-millimeter crack detection) and deploying semi-supervised learning in production, he blends rigorous academic methods with pragmatic engineering. Based in the Bay Area, he brings cross-disciplinary expertise from IC design to large-scale ML systems, with a knack for squeezing performance from constrained platforms.
8 years of coding experience
13 years of employment as a software developer
Bachelor of Science (BS), Electrical and Electronics Engineering, GPA: 3.20/4.00, Bachelor of Science (BS), Electrical and Electronics Engineering, GPA: 3.20/4.00 at National Taiwan University
Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering, GPA: 3.71, Doctor of Philosophy (Ph.D.), Electrical and Computer Engineering, GPA: 3.71 at Purdue University
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