Yun Chen

Yun Chen

Chief Scientist

Viggle AI

About Me

I am the Chief Scientist at Viggle AI, where I lead research on generative video models, from new model capabilities to production.

Previously, I was a founding member and Staff Researcher at Waabi, where I developed Waabi World, a generative closed-loop sensor simulator for self-driving. Before that, I was a Research Scientist at Uber ATG R&D. I am also completing my Ph.D. at the University of Toronto, advised by Prof. Raquel Urtasun.

My research focuses on generative models and world models: video generation, few-step distillation, and 4D reconstruction and simulation. I am passionate about finding simple, elegant solutions to challenging problems.

Beyond research, I am an advocate of Unix philosophy, a Linux enthusiast, and the author of the bestselling PyTorch book.

There should be one – and preferably only one – obvious way to do it. Although that way may not be obvious at first.

Research

Experience

 
 
 
 
 
Chief Scientist
Toronto, Canada

Leading research on generative video models.

  • Viggle-Animate: character replacement from a single edited frame, distilled to three forward passes
  • Meridian: geometry-guided video model with independent control of camera motion and scene time
  • Viggle-Turbo: 5x faster few-step distillation of Qwen-Image-2.1
 
 
 
 
 
Founding Member, Staff Researcher
Jun 2021 – Nov 2025 Toronto, Canada

Researcher II → Senior Researcher (2022) → Staff Researcher (2024).

  • Developed Waabi World, a generative closed-loop sensor simulator used to validate Waabi’s driverless launch
  • Built 4D world models: UniSim, G3R, Flux4D, SaLF
 
 
 
 
 
Research Scientist
Dec 2019 – Feb 2021 Toronto, Canada

Working closely with Prof. Raquel Urtasun and Prof. Shenlong Wang on 3D simulation.

  • 3D Reconstruction with large-scale weakly-labelled data in the wild
  • Photorealistic Image Simulation with Geometry-Aware Composition for Self-Driving
 
 
 
 
 
AI Resident
Sep 2018 – Dec 2019 Toronto, Canada

Working closely with Ming Liang and Bin Yang in ATG R&D for 3D Perception tasks.

  • Depth Completion: Densify LiDAR with image guidance, SOTA in KITTI
  • 3D Perception: 3D detection, tracking with multi-sensor. New SOTA in KITTI
  • Map structure learning with graph neural network. New SOTA in Argoverse motion forecasting
 
 
 
 
 
Research Intern
Mar 2018 – Jul 2018 Beijing, China

Working on Medical Imaging in Machine Intelligence Group led by Dr. Xian-Sheng Hua

  • Developed 3D R-CNN for CT/MRI, faster and more accurate than radiologist.
  • Explored weakly-supervised learning with limited labels and active learning for efficient labelling.