Jiankai Sun is a research-focused machine learning engineer and Stanford PhD student with nine years of experience building and publishing cutting-edge work in robotics, generative models, and efficient AI architectures. He has held research internships across top labs including NVIDIA, Meta FAIR, Microsoft, Autodesk, Baidu, and Tencent, and is lead author on a CoRL 2025 paper about hierarchical hybrid learning for long-horizon robotic assembly. His recent NeurIPS contributions address efficient reasoning and simulated attention for long generation, reflecting a focus on scaling model reasoning and long-horizon planning. Jiankai blends strong theory (MCMC, Langevin dynamics) with systems-level experimentation in real-world vision and robotics domains, and often moves ideas from simulation to embodied tasks. Based in Palo Alto, he brings a cross-disciplinary background spanning Stanford, CUHK, UCLA, and Shanghai Jiao Tong University, and a pattern of short, high-impact research engagements at premier industry and academic labs.
9 years of coding experience
1 year of employment as a software developer
The Chinese University of Hong Kong (CUHK)
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Shanghai Jiao Tong University
University of California, Los Angeles
Doctor of Philosophy - PhD, Doctor of Philosophy - PhD at Stanford University
Contributions:5 commits, 4 pushes, 1 branch in 4 years 1 month
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Jiankai Sun - Research Intern at Stanford University