Xiaojing Huang
Quantitative Analyst
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolXiaojing Huang is a quantitative analyst with a decade of experience applying machine learning and statistical modeling to finance, marketing, and AI products from the Bay Area. Currently Head of AI at General Robotics and a Quantitative Analyst at Google, she blends production ML work with deep quantitative rigor honed building portfolio risk systems and custom risk/reporting tools for institutional clients. Her background covers end-to-end solutions—from MATLAB and VBA risk engines for multi-billion dollar portfolios to SAS-driven marketing mix and user-level attribution analyses. She contributes to high-profile open-source ML projects like TensorFlow’s object detection module, where she improved visualization utilities and added LVIS/COCO evaluation and export capabilities. Xiaojing’s multidisciplinary training (MS in Industrial Engineering and a BS in Applied Math and Finance) underpins a pragmatic approach that translates research into deployable systems and clear client-facing insights. Colleagues describe her as a bridge between research-level models and production-ready analytics.
10 years of coding experience
2 years of employment as a software developer
BS, Applied Math, Finance, BS, Applied Math, Finance at American UniversityMS, Industrial Engineering and Management Sciences, MS, Industrial Engineering and Management Sciences at Northwestern University
Chinese
Stackoverflow
1,558reputation
114kreached
44answers
0questions
tensorflow
top-5%
machine-learning
top-5%
Github Skills (12)
object-detection10
computer-vision10
tensorflow10
python10
metric9
evaluation9
machine-learning9
neural-network6
object-detection-api6
deep-learning6
google-cloud-ml6
android6
Programming languages (2)
Github contributions (5)
tensorflow/models
Jun 2017 - Mar 2021
Models and examples built with TensorFlow
Role in this project:
ML Engineer
ML EngineerContributions:51 commits, 55 PRs, 46 pushes in 3 years 10 months
Contributions summary:Xiaojing primarily contributed to the object detection models within the TensorFlow framework. Their work involved refining visualization utilities within the object detection module, including adjusting fonts and line thicknesses in visualizations. They also implemented functionality related to COCO and LVIS evaluation metrics by incorporating options for supercategory aggregation, is_crowd flags, and LVIS-specific fields in the label map proto. Furthermore, the user added the capability to export detections in a format compatible with the LVIS server.
deep-learningtensorflow
jch1/iclr16
Nov 2015 - Feb 2016
Contributions:21 pushes, 1 branch in 3 months
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