Zicheng Liu
Global Strategy & Investor Relations Manager
Guangzhou City, Guangdong Province, China
Summary
👤
Senior🎓
Top SchoolZicheng Liu is a finance and technology professional with 10 years of experience blending quantitative research, equity investment, and strategic investor relations across leading Chinese asset managers and digital-entertainment firms. He holds an MEng in Financial Engineering from Cornell and dual BS degrees in Computer Science and Mathematics from UCLA, a background that underpins his work on generative AI-related equity research and algorithmic trading. At GF Asset Management he progressed from quantitative intern to VP covering TMT and AI investments, and now builds global strategy and partnerships for Huya’s gaming and digital entertainment ecosystem. He also contributes to open-source ML work, notably improving a PyTorch YOLOv3 implementation with bug fixes, data augmentation and testing—reflecting hands-on machine learning skills beyond his finance role. Based in Guangzhou, Zicheng combines rigorous quantitative modeling with practical product and investor-facing experience, often bridging technical teams and capital allocators.
10 years of coding experience
3 years of employment as a software developer
Master of Engineering - MEng, Financial Engineering, Master of Engineering - MEng, Financial Engineering at Cornell UniversityBachelor of Science - BS, Double Major in Computer Science and Mathematics of Computation, Bachelor of Science - BS, Double Major in Computer Science and Mathematics of Computation at University of California, Los Angeles
Chinese, Chinese, Chinese, English
Github Skills (8)
yolov410
object-detection10
computer-vision10
machine-learning10
pytorch10
data-augmentation10
python9
tensorflow3
Programming languages (9)
Github contributions (5)
BobLiu20/YOLOv3_PyTorch
Jun 2018 - Aug 2018
Full implementation of YOLOv3 in PyTorch
Role in this project:
ML Engineer
ML EngineerContributions:14 commits, 10 pushes, 1 branch in 2 months
Contributions summary:Zicheng primarily contributed to the YOLOv3 implementation in PyTorch by fixing bugs related to output filter configurations within the model. They added data augmentation techniques to improve the model's training process, demonstrating a focus on enhancing performance and generalization. Furthermore, the user added test scripts to evaluate the model's performance.
pytorchyolov3object-detection
BobLiu20/Classification_Nets
Jul 2017 - Nov 2017
Contributions:9 commits, 8 pushes, 1 branch in 4 months
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