Chengwei Zhang
Software Engineer at Intelligrated Inc.
Wuxi City, Jiangsu, China
Summary
🤩
Rockstar🎓
Top SchoolChengwei Zhang is a software engineer with 13 years of hands-on experience spanning embedded motor control, full-stack applications, and practical deep learning. He has built production-grade firmware for PMSM servo drives (FOC, encoder drivers, auto-tuning) and complementary Python PC tools using CANopen, while also developing distributed simulation and evolutionary design platforms for industrial clients. His work at Apple and Honeywell-caliber projects demonstrates strong cross-functional collaboration and international experience, including three years in the U.S. Chengwei is an active open-source contributor and ML practitioner—publishing object-detection training and OpenVINO deployment demos—and maintains a popular technical presence on Bilibili and a personal site with 26k annual users. He combines low-level control expertise with higher-level AI and visualization skills, and has repeatedly reduced costs and accelerated testing cycles through pragmatic hardware-software co-design.
13 years of coding experience
12 years of employment as a software developer
Master's, Electrical Engineering, 3.8/4.0, Master's, Electrical Engineering, 3.8/4.0 at University of CincinnatiBachelor's degree, Electrical and Electronics Engineering, 4.5/5.0, Bachelor's degree, Electrical and Electronics Engineering, 4.5/5.0 at Hohai University
Chinese, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
object-detection10
computer-vision10
pytorch10
machine-learning10
jupyter-notebook10
deep-learning10
tensorflow10
python10
openvino10
Programming languages (6)
Github contributions (5)
Tony607/object_detection_demo
Feb 2019 - Jun 2019
How to train an object detection model easy for free
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 18 commits, 16 pushes in 4 months
Contributions summary:Chengwei made several commits related to object detection, including updates to training notebooks, changes to the inference test script, and the addition of OpenVINO deployment scripts. They focused on model selection, configuration, and deployment, with a particular emphasis on using the OpenVINO toolkit for model optimization and inference benchmarking. The contributions involved adapting and integrating pre-trained models, reflecting practical experience with model deployment.
deep-learningobject-detectioncomputer-visionmachine-learningmask-rcnn
Tony607/mmdetection_object_detection_demo
Jun 2019 - Oct 2019
How to train an object detection model with mmdetection
Role in this project:ML Engineer
ML EngineerContributions:18 commits, 15 pushes, 1 branch in 3 months
Contributions summary:Chengwei primarily worked on training an object detection model using the mmdetection framework within a Jupyter Notebook environment. Their contributions involved modifying configuration files, adapting the codebase to custom datasets, and potentially evaluating model performance based on the commit messages and code changes. The user's focus was on customizing the training pipeline, specifically for custom COCO data, integrating it with a pre-trained model. This included installing dependencies and modifying the training script.
pytorchdeep-learningobject-detectioncomputer-visionmmdetection
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