Hang Zhang
Principal Engineer / Leading VLA Foundation Model Team @ XPENG
About
I am Hang Zhang, currently Head of the Foundation Model Team at XPeng, where I lead the development of next-generation autonomous driving systems using Vision-Language-Action (VLA) foundation models for L3 and L4 production vehicles. Previously, I was a Senior Staff Applied Research Scientist at Cruise, leading efforts in detection, segmentation, and perception model consolidation. Before Cruise, I was a Research Scientist at Meta, where I led the development of a scalable neural architecture optimization platform that supports AI models on Instagram, Portal, and VR headsets for tasks such as person understanding, AR / VR rendering, and ads ranking. Earlier in my career, I was a Senior Applied Scientist at Amazon AI, working on deep learning, computer vision, and the MXNet framework. During that time, we developed the ResNeSt model, which achieved state-of-the-art results on multiple vision benchmarks. My research has been cited more than 10,000 times in Google Scholar, and my open-source contributions have received more than 10,000 stars on GitHub. More about me https://hangzhang.org/
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United States
Computer Software
Deep Learning, Computer Vision, Machine Learning, Programming, C++
Experience
Principal Engineer | Director
XPENG
San Francisco Bay Area
Leading VLA Foundation Model Team.
Senior Staff Applied Scientist
Cruise
San Francisco Bay Area
· Leading efforts in Detection, Segmentation and Perception model consolidation. · Building a multi-task, multi-view, multi-modality, multi-frame and multi-platform model at Perception
Staff Applied Scientist
Cruise
San Francisco Bay Area
· Lead the efforts in model consolidation of camera major model on object detection and segmentation. · Lead the development of the Perception model training pipeline, which supports various projects on detection & segmentation, longtailed understanding, lane detection, offboard foundation model, and online distillation.
Research Scientist
Meta
Menlo Park, California, United States
· Lead the development of FBNAS project, a unified pipeline for cross-platform hardware-aware model optimization. FBNAS has been applied to several production models in person understanding on IG, AR/VR applications and Ads models. · Developed and open sourced D2Go toolkit, bringing Detectron2 to mobile [post] · Research on efficient architectures, e.g. FBNetV5, ScaleViT. Co-organizing workshop on “Computer Vision for MetaVerse” in ECCV2022.
Senior Applied Scientist
Amazon
California, United States
· Lead the development of GluonCV toolkit [https://cv.gluon.ai/] and AutoGluon toolkit (for AutoML) [https://auto.gluon.ai/]. · Lead research on large scale vision solution, e.g. ResNeSt, Bag-of-tricks, CFNet, dynamic SGD. [https://arxiv.org/pdf/2004.08955.pdf] · Organized 3 tutorials on ICCV19, CVPR20 and ECCV20. [https://hangzhang.org/CVPR2020/]
Applied Scientist Intern
Amazon Lab126
Cupertino, CA
• Developed state-of-the-art semantic segmentation algorithm of EncNet, which is published as an oral paper (~2.1%) in CVPR 2018.
Deep Learning Research Intern
NVIDIA
Holmdel, NJ
• Contributed to developing end-to-end deep learning approach for autonomous driving. • Implemented a Torch to Caffe model converter.
Education
Rutgers University
Computer Engineering
Southeast University
Automation
Hang Zhang's Contact Information
Phone
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