Pengfei Xiong
Quantitative Risk Developer
Shanghai, Shanghai, China
Summary
👤
Senior🎓
Top SchoolPengfei Xiong is a quantitative risk developer with 8 years of experience building production Python packages and models for credit demand forecasting, CCF, liquidity and stress-testing at large financial institutions including Citi and Pinpoint Asset Management. He holds a Master's in Financial Mathematics (4.0 GPA) and complements strong mathematical and statistical foundations with practical expertise in machine learning, time-series forecasting, Monte Carlo simulation and LightGBM/RNN implementations. Pengfei has a track record of translating research techniques (ARIMA-GARCH, GMM, Lasso/RF/XGBoost) into reproducible, auditable code and regulatory-ready documentation used in CCAR, CECL and IFRS9 workflows. His background spans both finance and scientific computing—from X-ray astronomy denoising with Poisson non-local PCA to enterprise risk automation—illustrating an ability to move between academic methods and production constraints. Comfortable presenting to technical and non-technical stakeholders, he also mentors new team members and led cross-functional projects that integrated model outputs into finance and risk reporting. Based in Shanghai, he is pursuing PhD-level interests in machine and deep learning while continuing to deliver scalable risk solutions.
8 years of coding experience
Master's degree, Financial Mathematics, 4.0, Master's degree, Financial Mathematics, 4.0 at North Carolina State UniversityBachelor of Science - BS, Astronomy, Bachelor of Science - BS, Astronomy at Xiamen UniversityHigh school, Science, High school, Science at No.1 Middle School Affiliated to Central China Normal University
English, Chinese
Github Skills (1)
latex3
Programming languages (1)
Github contributions (5)
xpf/Multi-Level-MMD-Regularization
Mar 2022 - Apr 2022
Contributions:21 commits, 18 pushes, 1 branch in 1 month
xpf/Data-Efficient-Backdoor-Attacks
Apr 2022 - Jun 2022
Contributions:33 commits, 32 pushes, 1 branch in 1 month
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