Jiacheng Zou is a scientist with 11 years of quantitative research and machine learning experience, currently working on autonomous mobility and delivery at Uber after research roles at Columbia and Stanford. He holds a PhD in Management Science & Engineering with a statistics minor from Stanford and has applied advanced methods—deep learning on multi-terabyte datasets, high-dimensional inference, and mechanism design—to problems in finance, healthcare, and market design. His recent work on supply chain graph learning (R&R at JFE) complements prior projects on organ allocation, asset pricing with conditional inference, and credit-risk deep learning modeling. Comfortable bridging theory and production, he combines rigorous academic publication and conference exposure (NeurIPS, INFORMS, NBER-affiliated venues) with hands-on engineering of GPU-based training pipelines. Based in San Francisco, he blends statistical rigor, mechanism-design insight, and practical ML systems experience to tackle complex, high-stakes decision problems.
11 years of coding experience
9 years of employment as a software developer
High School Natural Sciences, High School Natural Sciences at Northeast Yucai High School
Bachelor’s Degree Financial Engineering, Bachelor’s Degree Financial Engineering at Tianjin University of Finance and Economics
Doctor of Philosophy - PhD Management Sciences & Engineering; PhD minor Statistics, Doctor of Philosophy - PhD Management Sciences & Engineering; PhD minor Statistics at Stanford University
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