Summary
Jinxuan Tang is a data-driven electrical engineer and machine learning practitioner with a decade of hands-on experience spanning signal processing, computer vision, and hardware design. Currently an MS student at Columbia (4.0 GPA) and Lead Data Science Intern at AI Camp, he has built production CV systems like a YOLOv5-based plant diagnosis pipeline and optimized multi-style image transfer services, cutting inference time by ~50%. His background blends analog/digital circuit and FPGA design with large-scale software skills (Python, PyTorch, TensorFlow, GCP/AWS, Spark), enabling end-to-end solutions from sensor capture to cloud deployment. Notably, he has shipped a compact DC-DC converter design at Siemens and led a face-recognition research team at the Chinese Academy of Sciences achieving 98.8% on Yale Face Database. Curious and versatile, he combines contest-winning debate and leadership awards with a pragmatic focus on applied ML and embedded systems integration.
10 years of coding experience
Master of Science - MS, Electrical Engineering, specializing in Data-Driven Analysis and Signal Processing, 4.0/4.0, Master of Science - MS, Electrical Engineering, specializing in Data-Driven Analysis and Signal Processing, 4.0/4.0 at Columbia University in the City of New York
Bachelor's degree, Electronic Information Science and Technology, 3.82/4.0, Bachelor's degree, Electronic Information Science and Technology, 3.82/4.0 at University of Electronic Science and Technology of China
Chinese, English