Zheng Tang
Senior Deep Learning Engineer, Metropolis at NVIDIA
Redmond, Washington, United States
Summary
👤
Senior🎓
Top SchoolZheng Tang is a Senior Deep Learning Engineer with nine years of experience building large-scale, multi-camera tracking and identity systems, currently leading Metropolis efforts at NVIDIA. He designed a Multi-Camera Tracking AI Workflow showcased in NVIDIA GTC’24, created a 3.2M-image human-centric dataset, and trained ReID transformers that boosted tracking KPIs by 6–20%. Previously an Applied Scientist on the Amazon One team, he helped launch palm-based identity services and filed multiple U.S. patents for multimodal identification and update mechanisms. Zheng’s research roots include CVPR- and ICCV-accepted benchmarks and pose-aware ReID models, and he organizes AI City Challenges using the largest indoor synthetic dataset from Omniverse. His work uniquely bridges academic rigor and production impact—turning synthetic data and novel architectures into measurable real-world gains.
8 years of coding experience
8 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Electrical & Computer Engineering, Doctor of Philosophy (Ph.D.) Electrical & Computer Engineering at University of Washington
English, Chinese, Chinese, Spanish
Github Skills (43)
convolutional-neural-networks10
pose10
nvidia10
computer-vision10
face9
multi-task-learning9
darknet9
deep-learning9
re-identification8
human-pose-estimation8
jekyll8
caffe8
openpose8
machine-vision7
action-recognition7
Programming languages (7)
Github contributions (5)
zhengthomastang/zhengthomastang.github.io
Jan 2020 - Dec 2022
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Contributions:275 commits, 492 pushes, 22 branches in 2 years 11 months
templategithub-pages-templatemmistakesmistakesjekyll
zhengthomastang/Cal_PnP
Oct 2018 - Feb 2022
Contributions:22 commits, 18 pushes, 1 branch in 3 years 4 months
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