Kyle Zheng
Sunnyvale, California, United States
Summary
🤩
Rockstar🎓
Top SchoolShuai Zheng is a Senior Applied Research Scientist with 12 years of experience building and deploying large-scale multimodal and vision-language systems, currently leading scalable perception R&D for autonomous vehicles at Cruise. He bridges foundational research and production engineering—publishing 20+ papers in top venues like CVPR/ICCV/ECCV and shipping edge and cloud AI products across companies including eBay, Verkada, and DawnLight. His work spans temporal multi-task systems, low-power on-device models, and large-scale product recognition (including Cloud TPU/Pod pipelines), reflecting both deep academic rigour from a DPhil at Oxford and hands-on deployment experience. Shuai contributes to open-source research code (e.g., updating the influential CRF-RNN demo) and serves extensively as a reviewer and PC member, signaling a strong community leadership role. Notably, he has driven winning low-power vision competition submissions and holds multiple patent applications tying research to practical IP.
12 years of coding experience
5 years of employment as a software developer
DPhil, Engineering Science (Computer Vision), DPhil, Engineering Science (Computer Vision) at University of OxfordMaster of Engineering (MEng), Pattern Recognition and Intelligent System (Computer Vision), 3.5/4, Master of Engineering (MEng), Pattern Recognition and Intelligent System (Computer Vision), 3.5/4 at Graduate University of Chinese Academy of SciencesBachelor of Engineering (BEng), Information Engineering (Photoelectronic), 3.72/4, Bachelor of Engineering (BEng), Information Engineering (Photoelectronic), 3.72/4 at Beijing Institute of Technology
English, Chinese, German, Spanish
Github Skills (11)
computer-vision10
machine-learning10
image-segmentation10
caffe10
deep-learning10
segmentation10
python10
github9
git-repository9
git9
tensorflow3
Programming languages (6)
Github contributions (5)
torrvision/crfasrnn
Oct 2015 - Jul 2017
This repository contains the source code for the semantic image segmentation method described in the ICCV 2015 paper: Conditional Random Fields as Recurrent Neural Networks. http://crfasrnn.torr.vision/
Role in this project:
ML Engineer
ML EngineerContributions:2 releases, 48 commits, 4 PRs in 1 year 9 months
Contributions summary:Kyle primarily focused on modifying and updating the example Python script for the CRF-RNN model. They added timing functionalities to measure performance and debugged issues related to the visualization of segmentation results. The user also updated the script to resize the input image and incorporate changes from the upstream Caffe base, indicating an understanding of the underlying deep learning framework and model deployment. These changes centered around optimizing the demo script and resolving visualization bugs.
pytorchiccvrecurrentsegmentationvision
bittnt/ImageSpirit
Mar 2015 - Aug 2015
Contributions:11 commits, 4 pushes in 5 months
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