Yiang Weng
Credit Analyst at CNPC FINANCES
Beijing, China
Summary
🤩
Rockstar🎓
Top SchoolYiang Weng is a credit and capital management analyst with four years of experience blending corporate finance, risk management, and data-driven automation to support lending and treasury decisions. Based in Beijing, Yiang has led end-to-end credit due diligence and monitoring for natural gas and renewable energy projects while facilitating guarantees and diverse loan products for more than ten counterparties. He introduced Python and VBA tooling that cut FX data collection and ledger processing time dramatically, improving operational accuracy and freeing traders for higher-value work. A Columbia MA Statistics graduate with a background in economics from UW–Madison, Yiang pairs quantitative modeling skills with practical finance judgment. Uncommonly for a credit analyst, he also contributes to ML engineering—working on industrial-grade object detection (YOLOv6) and hardware-aware neural design—bringing a technical edge to risk analytics and model deployment. This combination of hands-on coding, financial structuring, and sector experience in energy and commodities makes him effective at turning complex data into actionable credit and capital strategies.
3 years of coding experience
Bachelor's degree Economics, Bachelor's degree Economics at UW-MadisonBachelor of Science - BS Economics Consumer Behavior & Marketplace Studies, Bachelor of Science - BS Economics Consumer Behavior & Marketplace Studies at University of Wisconsin-MadisonMaster of Arts - MA Statistics, Master of Arts - MA Statistics at Columbia University
Github Skills (9)
model-building10
object-detection10
computer-vision10
pytorch10
python10
modeling10
model-driven10
model-driven-development10
onnx9
Programming languages (3)
Github contributions (5)
meituan/YOLOv6
Jun 2022 - Aug 2022
YOLOv6: a single-stage object detection framework dedicated to industrial applications.
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 22 PRs, 12 pushes in 1 month
Contributions summary:Yiang primarily contributed to the development of the YOLOv6 object detection framework. Their work involved adding and modifying core components, including the `EffiDeHead`, and other supporting files such as `loss.py`, and `efficientrep.py`, indicating involvement in model architecture and loss function adjustments. The user was also involved in exporting the model to ONNX and updating the evaluation components.
pytorchyolov6deep-learningdedicatedindustrial
khwengXU/YOLOv6
Jul 2022 - Aug 2022
YOLOv6: a single-stage object detection framework dedicated to industrial applications.
Contributions:3 pushes, 9 branches in 29 days
pytorchyolov6deep-learningdedicatedindustrial
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