Tianjiao Ding is a research-focused machine learning engineer with 11 years of experience bridging rigorous mathematics and practical systems to solve robustness, efficiency, and unsupervised learning problems. Currently a Research Assistant at UPenn (transferred from JHU) and formerly an Applied Scientist at AWS, she has developed provably robust algorithms for corrupt inputs, state-of-the-art unsupervised clustering on CIFAR/ImageNet, and zero-shot plug-and-play methods that steer LLM and diffusion model behavior via sparse coding of latent activations. Her work includes a first large-scale concept dataset (40,000 concepts, 1.2M context sentences), variational spectral forms enabling linear-time transformers, and efficient 3D reconstruction and motion estimation methods that rival RANSAC at much lower cost. Comfortable in both theory and implementation, she repeatedly turns abstract mathematical guarantees into practical gains for vision and language models. Based in Philadelphia, she blends deep academic training with industry research experience to deliver reproducible, high-impact ML innovations.
11 years of coding experience
7 years of employment as a software developer
Bachelor of Engineering - BE Computer Science, Bachelor of Engineering - BE Computer Science at ShanghaiTech University
Johns Hopkins University
Doctor of Philosophy - PhD Computer and Information Sciences General, Doctor of Philosophy - PhD Computer and Information Sciences General at University of Pennsylvania
Contributions:38 commits, 2 PRs, 7 pushes in 11 months
scrapyspiderrustspider-frameworkchen
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.
Request Free Trial
Tianjiao Ding - Research Assistant at University of Pennsylvania