Zheng Fang is a Senior Staff Research Scientist in Decision Sciences with 10+ years building optimization, forecasting, and ML systems for large-scale ad platforms and public-safety infrastructure. He owns end-to-end delivery—from algorithm research and validation to production rollout—specializing in scalable ad delivery, pacing, bidding, and budget-control algorithms that drive measurable business impact. Previously an Associate Director, he pairs deep individual-contributor expertise with mentorship and cross-functional leadership, having guided full-time data scientists and interns on production-grade systems. His background spans big-data platforms (Spark, Hive, Kafka), cloud pipelines, and pragmatic AI-augmented development to accelerate engineering work. Notably, his early research produced fast numerical solvers for elastodynamics and he shipped practical tooling from front-end drag-and-drop UI work to backend optimization that reduced manual diagnostics from days to hours.
10 years of coding experience
13 years of employment as a software developer
BS, Computational and Applied Mathematics, BS, Computational and Applied Mathematics at University of Science and Technology of China
Master's degree, Computer Science, Master's degree, Computer Science at University of Illinois Chicago
Master degree, Applied Mathematics, Master degree, Applied Mathematics at University of Illinois at Chicago
Contributions:4 releases, 165 commits, 4 PRs in 1 year 6 months
Contributions summary:Zheng primarily contributed to the development of a draggable layout system using React. Their work focused on implementing core features related to drag-and-drop functionality, including bounds detection, fixing bugs related to element movement, and creating a grid-based layout prototype. The user's commits show a strong focus on UI component development and user interaction logic.
Contributions:48 commits, 43 pushes, 1 branch in 1 year
reactdragger2draggable-windowdragdraggable
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