Ziyuan Zhong is a quantitative researcher and PhD candidate in computer science based in New York, specializing in robustness, testing, and fairness of deep learning systems for autonomous driving and image classification. With 11 years of experience spanning academia and industry, he has built novel adversarial and evolutionary testing methods, language-guided diffusion models for realistic traffic generation, and practical neuron-coverage techniques to detect classifier bias. He combines strong Python proficiency and familiarity with C/C++ with end-to-end software development experience across research codebases, web apps, and Matlab packages. His internships at NVIDIA and Baidu informed industry-grade evaluations of ADS fusion and controllable traffic simulation, and he currently applies that expertise at Squarepoint while exploring startup investing. Notably, his work shows both theoretical insight—demonstrating invariance properties of group fairness under label corruption—and practical impact in generating critical driving scenarios. He is open to research collaborations on testing autonomous driving systems and translating robustness research into deployable evaluation tools.
10 years of coding experience
8 years of employment as a software developer
Bachelor’s Degree, Mathematics(Computer Science concentration), Bachelor’s Degree, Mathematics(Computer Science concentration) at Reed College
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Columbia Engineering
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at Columbia University
Contributions:34 PRs, 372 pushes, 29 branches in 1 year 6 months
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