Ting King - Technologent at AppView

Ting King

Technologent at AppView

Seattle, Washington, United States

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Summary

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Rockstar

🎓

Top School

Ting King is a technology leader and full‑stack engineer with six years of professional experience driving application architecture, modernization, and data-driven tooling from Seattle. At AppView she introduced the organization’s first standard web application framework, modernized enterprise site management to double deployment and maintenance efficiency, and led UAT and defect management while coaching project teams. Her background spans backend, frontend, and data work—MongoDB analytics, automated CFO reporting, AWS/NodeJS/Python—and earlier roles included full‑stack and ML-adjacent responsibilities at Alibaba. An active open-source contributor, she has improved loss functions and training utilities in PaddleViT and enhanced documentation, pipelines, and demos across prominent OpenMMLab detection projects like MMDetection and MMYOLO. Collectedly, she blends practical production delivery with a detail-oriented open-source mindset, often fixing subtle bugs and usability issues that improve team velocity.

6 years of coding experience

2 years of employment as a software developer

University of California, Los Angeles

Bachelor's degree, computer science and Technology, 3.86, Bachelor's degree, computer science and Technology, 3.86 at Zhejiang University

Github Skills (21)

pytorch10

loss-functions10

python10

deeplearning-ai10

deep-learning10

object-detection10

paddlepaddle10

computer-vision10

transformer10

documentation10

datasets9

classification9

yolov58

yolov88

debug8

Programming languages (10)

TypeScriptJavaCSSC++BatchfileVueJavaScriptHTML

Github contributions (5)

open-mmlab/mmyolo

Sep 2022 - Jan 2023

OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8,YOLOX, PPYOLOE, etc.

Role in this project:

Full-stack Developer

Contributions:175 reviews, 38 commits, 53 PRs in 4 months

Contributions summary:Ting primarily contributed to documentation, code refinement, and feature enhancements within the YOLO object detection framework. Their work involved optimizing the display of Read the Docs pages, refining configuration files, and correcting variable names within the base YOLONeck module. They also addressed training errors for the YOLOX Nano model and implemented a video demo feature, showcasing a range of contributions spanning documentation, model implementation, and usability improvements.

pytorchrtmdetyolov6toolboxyolov8

open-mmlab/mmdetection

Feb 2022 - Jan 2023

OpenMMLab Detection Toolbox and Benchmark

Role in this project:

Back-end Developer & Documentation Contributor

Contributions:50 reviews, 7 commits, 14 PRs in 10 months

Contributions summary:Ting contributed to the documentation, fixing typos and updating markdown files related to configuration and finetuning. They also made code enhancements, such as adding a probability parameter to the Mosaic transform within the dataset pipelines. Additionally, they fixed bugs related to model analysis and deprecated old type aliases for numpy.

retinanetbenchmarkfast-rcnnopenmmlabsemisupervised-learning

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