Yijia Weng
Analyst at NielsenIQ
Shanghai, Shanghai, China
Summary
🤩
Rockstar🎓
Top SchoolYijia Weng is an analyst with nine years of data and analytics experience, currently at NielsenIQ in Shanghai and holding an MS in Business Analytics from the University of Rochester. She blends rigorous quantitative training in economics and mathematics with hands-on skills in SQL, R, Excel and Tableau to drive operational improvements and data-driven recommendations across product and nonprofit engagements. Her background spans audit and financial services at PwC to product-focused data work at Hellobike, where her analyses helped increase system availability from 90% to 95%. As a contributor to a SIGGRAPH 2020–related deep learning repository, she has practical machine learning engineering experience modifying model architectures and training pipelines for 3D motion editing. Colleagues would describe her as technically versatile, client-facing, and adept at turning large, messy datasets into actionable insights.
9 years of coding experience
Master of Science - MS, Business Analytics, 3.56/4.0, Master of Science - MS, Business Analytics, 3.56/4.0 at University of Rochester - Simon Business SchoolEconomics and Mathematics, 3.72/4.0, Economics and Mathematics, 3.72/4.0 at University of ConnecticutBachelor of Arts - BA, Economics and Mathematics, 3.4/4.0, Bachelor of Arts - BA, Economics and Mathematics, 3.4/4.0 at Boston University
Github Skills (12)
pytorch10
machine-learning10
deep-learning10
trainings10
computer-graphics10
python10
modeling10
style-transfer10
animate9
animation9
web-animations9
tensorflow3
Programming languages (3)
Github contributions (5)
DeepMotionEditing/deep-motion-editing
May 2020 - Apr 2021
An end-to-end library for editing and rendering motion of 3D characters with deep learning [SIGGRAPH 2020]
Role in this project:
ML Engineer
ML EngineerContributions:13 commits, 4 PRs, 1 push in 11 months
Contributions summary:Yijia primarily focused on modifying the `style_transfer` directory, indicating involvement in model development and training. They updated configuration files, data loading scripts, and scripts for testing and demo figures. Additionally, the user made changes to the `networks.py` file, suggesting work on the core neural network architecture.
style-transferend-to-endcomputer-animationdeep-learningoptical-flow
HalfSummer11/CAPTRA
Mar 2021 - Oct 2021
[ICCV 2021, Oral] Official PyTorch implementation of CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds
Contributions:39 commits, 1 PR, 19 pushes in 7 months
pytorchiccvarticulated-objectspoint-cloudscategory
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