Mingxing Tan - Research Scientist TLM at Waymo

Mingxing Tan

Research Scientist TLM at Waymo

Mountain View, California, United States

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Summary

🤩

Rockstar

🎓

Top School

Mingxing Tan is a research scientist and TLM with eight years of experience building and optimizing machine learning models and compilers at Google and Waymo, based in Mountain View. He has deep hands-on expertise in object detection and efficient model design, contributing to widely used projects like EfficientDet in TensorFlow and Google AutoML and improving TPU and edge deployment workflows. Mingxing also works on low-level ML infrastructure, enhancing the XLA compiler’s GPU codepaths and fusion/reduction logic to boost runtime efficiency. His background in computer engineering from Cornell and computer science from Peking University complements a profile that bridges research, productionization, and systems-level optimization. Notably, his open-source contributions touch both high-level model innovations and backend compiler improvements—an uncommon combination that helps move models from prototype to performant deployment.

9 years of coding experience

Computer Science, Computer Science at Peking University

Computer Engineering, Computer Engineering at Cornell University

Github Skills (24)

c-language10

python10

gpu-programming10

machine-learning10

deep-learning10

tensorflow10

xla10

compiler10

computer-vision10

object-detection10

cprogramming-language10

efficientnet10

image-processing9

operation9

convolutional-neural-networks9

Programming languages (3)

C++Jupyter NotebookPython

Github contributions (5)

google/automl

Mar 2020 - Aug 2022

Google Brain AutoML

Role in this project:

ML Engineer

Contributions:116 reviews, 415 commits, 261 PRs in 2 years 5 months

Contributions summary:Mingxing primarily contributed to the implementation and release of EfficientDet, as evidenced by the commit messages and code changes. They worked on the anchor definition, box decoding, and non-maximum suppression, indicating expertise in object detection algorithms. The user also focused on model post-processing, making changes related to detections, leading to the inclusion of a new feature for mixed precision for inference.

automlefficientdetobject-detectionefficientnetefficientnetv2

tensorflow/tpu

May 2019 - Mar 2021

Reference models and tools for Cloud TPUs.

Role in this project:

ML Engineer

Contributions:55 commits, 12 PRs, 30 pushes in 1 year 10 months

Contributions summary:Mingxing primarily contributes to the codebase by updating and improving the AutoAugment utilities and working on the EfficientNet models. Their commits focus on incorporating new policies, and modifying the model architecture. The changes also include modifications related to model export and the inclusion of new functionalities for edge devices.

cloud

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