Summary
Shuai Tang is a quantitative researcher with a Ph.D. in Cognitive Science from UC San Diego and a decade of experience applying representation learning and ML to real-world systems. He has transitioned from academic work in unsupervised and neural representation learning to applied roles at AWS—contributing to Transformer-based generation for CodeWhisperer and differential privacy in AWS Clean Rooms—and now conducts quantitative research at Jump Trading. Shuai blends rigorous theory (unsupervised learning, sparse representations, optimization) with production-focused applied science, shipping models and privacy-aware systems at scale. His background includes multiple industry research internships (Microsoft, Adobe, Tencent) and teaching deep learning and unsupervised learning courses, signaling both breadth and the ability to communicate complex ideas. Based in New York, he brings a researcher’s curiosity to high-frequency decision environments, often approaching model design with efficiency and interpretability in mind.
10 years of coding experience
12 years of employment as a software developer
University of California, San Diego
Bachelor of Science (B.S.) Information Science and Communication Engineering, Bachelor of Science (B.S.) Information Science and Communication Engineering at Zhejiang University
English, Chinese