Published multiple papers, including 'Improving Chain-of-Thought Efficiency for Autoregressive Image Generation' and 'LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity', in top conferences like CVPR and ICML.
Research Experience
Works as an AI research scientist at Meta Superintelligence Labs, contributing to the development and training of various media foundation models such as emu and MovieGen.
Education
Ph.D. from Harvard University, advised by Prof. Todd Zickler; B.A.Sc from the University of Toronto, advised by Prof. Sven Dickinson and Prof. Sanja Fidler.
Background
A staff AI research scientist at Meta Superintelligence Labs, a core contributor to training Meta's media foundation models including emu, MovieGen, and multimodal image generations. Also works on personalized media generation. Previously worked on depth estimation, 3D computer vision, on-device computer vision, and human perception-inspired computational vision.
Miscellany
Contact: jialiangwang05@gmail.com / LinkedIn / Google Scholar