WorldEngine AI
2026 · Santa Clara, CAResearch Intern
Currently looking for full-time/internship opportunities.
I am a 3rd-year Ph.D. student at Texas A&M University, supervised by Prof. Wenping Wang and Prof. Xin Li. I also work closely with Prof. Yue Wang. People call me John.
I am passionate about bringing intelligence to the real world, through Robot Foundation Models, Physics-based Motion, and 3D Real-to-Sim.
Previously, I obtained my B.Eng. and M.Eng. degrees from Shanghai Jiao Tong University, where I was fortunate to be supervised by Prof. Li Song.
A budget-aware benchmark where agents rewrite their own harness between rounds to improve at a task. We ran 18 frontier models on 24 vision and robotics tasks to see where self-improvement pays off.
A humanoid policy steered by future goal images: a predictor turns subtask prompts into goal images, and a planner drives whole-body actions toward them across multi-task routines.
We learn dense rewards from action-free videos by modeling task-relevant latent dynamics, so the reward tracks task progress rather than visual distractors such as lighting.
Developed QOED to enhance robot exploration by isolating identifiable model parameters and suppressing unidentifiable 'nuisance' directions in high-dimensional systems.
PDT is a novel diffusion-based framework for transforming dense point distribution into a target distribution that is semantically meaningful.
In this paper, we introduced a novel text-to-avatar generation method that separately generates the human body and the clothes and allows high-quality animation on the generated avatar.
We proposed a novel diffusion-based pipeline that generates complete 360 panoramas using one or more unregistered NFoV images captured from arbitrary angles.
SPGen leverages Spherical Projection (SP) to generate high-quality 3D shapes with diffusion models.
Research Intern
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Media Graphics Software Engineering Intern
I love comedy, sports, music, history, and cinema.