I am someone who works at the intersection of mathematics, computer science, data science, and quantitative finance. I am currently a Data Science Intern at Tencent (Level Infinite, its global gaming brand) in Palo Alto, where I apply statistical modeling, machine learning, and A/B testing to turn large, complex datasets into data-driven insights across game development, marketing, and operations. Alongside this, I am pursuing graduate studies at the University of Pennsylvania and UC Berkeley, and I am passionate about data modeling, machine learning, and AI-driven systems. I enjoy building practical, scalable solutions and exploring how intelligent models can solve real-world problems.
Master of Analytics
Dual Degree: M.S.E. in Data Science & Computer and Information Technology
M.S. in Quantitative Finance
B.Econ. in Finance
Tencent
Global Key Advisors
Haitong Securities
Tsingtao Stone Asset Management
Engineered a high-performance PyTorch-style tensor and autograd engine in C++/CUDA, achieving 2.3× faster convolution and 3.7× faster pooling than LibTorch on megapixel-scale inputs. Built a full GPU training stack with custom-optimized kernels for convolution, RNN/LSTM, normalization, pooling, and self-attention, enabling 1–7 ms forward/backward passes. Designed distributed LSTM/ML forecasting pipelines using NCCL-based data parallelism, delivering a 25% error reduction over baseline models in financial backtests.
Engineered a financial reasoning system integrating GPT-4 and Claude Sonnet APIs to analyze complex derivative pricing scenarios, applied chain-of-thought, few-shot, and instruction-tuning strategies for a 55% performance lift over zero-shot baselines, and outperformed traditional pricing models by 40% and ML baselines by 15-60%.