01Quant
A quantitative beginning
My journey began at Wuhan University, where studying Financial Engineering first introduced me to AI. I started by applying machine learning to quantitative investing. The encouraging results mattered, but the bigger revelation was that AI's potential extended far beyond predicting markets.
02AI Foundations
Learning from first principles
For graduate school, I took a less conventional path and joined Stanford ICME. Its interdisciplinary training across applied mathematics, statistics, and computer science gave me a rigorous understanding of machine learning and shaped the first-principles approach I still use to evaluate technologies today.
03AI at Scale
Building for hundreds of millions
After Stanford, I joined ByteDance, one of the world's largest AI-native technology companies, to work on core recommendation systems for Toutiao, Douyin, and TikTok. My work combined Transformer-based sequence modeling, multi-objective learning, and online experimentation to better understand user behavior and long-term engagement at scale.
04Investing
Backing technology in motion
Stanford's culture of innovation stayed with me. I wanted a broader vantage point on how frontier AI becomes an enduring product and company in the hands of exceptional founders. That conviction led me into investing: to discover, understand, and support entrepreneurs using technology to create meaningful change.
