Nathon Lee

I’m DeWang Li (nathon-lee), a systems engineer and independent researcher exploring how large-scale AI systems are built, optimized, and aligned. My work spans distributed infrastructure, LLM training and serving, reinforcement learning, and performance engineering. I enjoy bridging the gap between systems research and real-world production environments. This site serves as a collection of my projects, research notes, open-source contributions, and ongoing explorations in AI systems.

Email  /  CV  /  Bio  /  Github /  Blogs

Research

I'm interested in computer vision, deep learning, generative AI, and image processing. Most of my research is about inferring the physical world (shape, motion, color, light, etc) from images, usually with radiance fields. Some papers are highlighted.

We present Budgeted Human Steering, a framework for improving long-horizon agent performance under constrained human supervision. Instead of relying on dense feedback, our method identifies critical decision points where human intervention yields the highest utility and distills corrective signals into the agent's policy through online steering distillation. This enables agents to progressively internalize human guidance, reducing supervision requirements while maintaining strong task performance across complex multi-step environments.

Carefully casting reflection rays lets us synthesize photorealistic specularities in real-world scenes.

A more physically-accurate inverse rendering system based on radiance caching for recovering geometry, materials, and lighting from RGB images of an object or scene.

Miscellanea

Visitors

Thanks for visiting my homepage.

Design and source code from Jon Barron's website