Yu-Guan Hsieh is an AI researcher and applied mathematician with nine years of experience specializing in generative models, vision-language pretraining, efficient fine-tuning, and learning in games. After a PhD in optimization and machine learning and top-tier internships (AWS, RIKEN) he held a postdoc at Apple where he led the Graph-Based Captioning project and co-authored multiple 2024 arXiv/ICML papers. Now based in Tokyo as AI Anime Researcher at Spellbrush, he blends rigorous ML research with a creative mission to make anime more realistic using state-of-the-art generative techniques. His work spans theory and practice—from extragradient convergence proofs to building datasets and released code—showing a rare fluency across math, systems, and production research. Notably, he has published on using diffusion priors for bandit decision-making and on practical improvements to flow models and gradient surgery, signaling an aptitude for translating theoretical ideas into applied ML.
A 99% automatized pipeline to construct training set from anime and more for text-to-image model training
Contributions:61 commits, 82 PRs, 257 pushes in 3 months
pytorchpipelinesemi-automaticdeep-learningsemi
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.