Jianwei Yang
Member Of Technical Staff at xAI
Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolJianwei Yang is a research scientist with 11 years of experience in computer vision and multimodal AI, now building agentic multimodal foundations at Meta after leading multi-modal and AGI work at Microsoft Research. He holds advanced AI training (PhD-level study at Georgia Tech and Virginia Tech) and maintains influential open-source projects—most notably faster-rcnn.pytorch and graph-rcnn.pytorch—which are widely used in detection and scene-graph research. Jianwei blends research and engineering, contributing not just model architectures and loss functions but also performance-focused systems work such as GPU-accelerated NMS, RPN/ROI adaptations, image trimming, and efficient mini-batch loading. Based in Redmond, he is comfortable moving ideas from ECCV papers to production-ready code, bridging academic rigor with practical, scalable implementations.
11 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD Artificial Intelligence, Doctor of Philosophy - PhD Artificial Intelligence at Georgia Institute of Technology
Github Skills (12)
model-building10
gpu-programming10
computer-vision10
pytorch10
machine-learning10
deep-learning10
faster-r-cnn10
modeling10
model-driven10
model-driven-development10
back-end-development8
python8
Programming languages (6)
Github contributions (5)
jwyang/graph-rcnn.pytorch
Jul 2018 - Mar 2020
Pytorch code for our ECCV 2018 paper "Graph R-CNN for Scene Graph Generation" and other papers
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:198 commits, 7 PRs, 180 pushes in 1 year 9 months
Contributions summary:Jianwei primarily contributed to the implementation of a scene graph generation model, which involved the creation of a baseline model. The code changes included adding the necessary model, loss function, and data loading components for the project. They worked on defining the model architecture and setting up the training process for the scene graph generation task, utilizing PyTorch for machine learning implementation.
pytorchdeep-learningeccvr-cnneccv-2018
jwyang/faster-rcnn.pytorch
Aug 2017 - May 2020
A faster pytorch implementation of faster r-cnn
Role in this project:Back-end Developer
Back-end DeveloperContributions:312 commits, 49 PRs, 255 pushes in 2 years 10 months
Contributions summary:Jianwei primarily focused on integrating and adapting a PyTorch implementation of Faster R-CNN for GPU processing. Their work involved adding a PyTorch-based non-maximum suppression (NMS) library and modifying the NMS wrapper to support the GPU version. They adapted various RPN and ROI layers to utilize the GPU-accelerated NMS. Additional contributions involved incorporating image trimming and mini-batch loading to the project.
pytorchr-cnnfasterpytorch-implementationfaster-rcnn
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