Liang-Chieh (Jay) Chen- Home Page

Liang-Chieh (Jay) Chen Research Scientist, Apple AI/ML Email: lcchen at cs dot ucla dot edu [google-scholar]

About Me

I am currently a Research Scientist at Apple AI/ML, building cutting-edge visual generative models.

I am widely recognized for my contributions to the DeepLab series (co-developed with George Papandreou): DeepLabv1, DeepLabv2, DeepLabv3, DeepLabv3+. Since December 2014, our introduced atrous convolution (also known as convolution with holes or dilated convolution) has become a foundational technique for dense prediction tasks.

Influential DeepLab derivatives include Auto-DeepLab, Panoptic-DeepLab (winning method of the Mapillary Vistas Panoptic Segmentation track at ICCV 2019, and top performer on Cityscapes leaderboards), Axial-DeepLab, ViP-DeepLab, MaX-DeepLab, and kMaX-DeepLab.

I am also known for my collaborative work on MobileNetv2 and MobileNetv3, which have become standards for efficient neural network design on mobile devices.

Previously, I was a Senior Principal Scientist at Amazon in 2025, and a Research Scientist and Manager at ByteDance Research/TikTok from 2023 to 2025. Prior to that, I spent seven years as a Research Scientist at Google Research in Los Angeles (2016–2023). I received my Ph.D. in Computer Science from the University of California, Los Angeles, in 2015, advised by Alan L. Yuille. I received my M.S. in Electrical and Computer Engineering from the University of Michigan, Ann Arbor.

News

Activities

• Area Chair for ICCV 2019, CVPR 2020/2023/2024, ECCV 2020/2024, NeurIPS 2022/2024/2025, ICML 2025/2026.

• Senior/Lead Area Chair: ICCV 2025, CVPR 2025/2026, ECCV 2026, NeurIPS 2026.

• Action Editor for TMLR 2025/2026.

• Reviewer for CVPR, ECCV, ICCV, NeurIPS.

  • Outstanding reviewer in CVPR 2021

  • Top reviewer in NeurIPS 2019

  • Outstanding reviewer in CVPR 2018

Selected Recent Publications

Taming Outlier Tokens in Diffusion Transformers Xiaoyu Wu, Yifei Wang, Tsu-Jui Fu, Liang-Chieh Chen, Zhe Gan, Chen Wei Technical Report. [preprint (arxiv: 2605.05206)]

Large Language Models are Universal Reasoners for Visual Generation Sucheng Ren, Chen Chen, Zhenbang Wang, Liangchen Song, Xiangxin Zhu, Alan Yuille, Liang-Chieh Chen, Jiasen Lu Technical Report. [preprint (arxiv: 2605.04040)]

Autoregressive Image Generation with Masked Bit Modeling Qihang Yu, Qihao Liu, Ju He, Xinyang Zhang, Yang Liu, Liang-Chieh Chen*, Xi Chen* (*equal advising) In International Conference on Machine Learning (ICML), Seoul, South Korea, July 2026. [preprint (arxiv: 2602.09024)] [project website] [code]

A Frame is Worth One Token: Efficient Generative World Modeling with Delta Tokens (highlight) Tommie Kerssies, Gabriele Berton, Ju He, Qihang Yu, Wufei Ma, Daan de Geus*, Gijs Dubbelman*, Liang-Chieh Chen* (*equal advising) In Conference on Computer Vision and Pattern Recognition (CVPR), Denver, Colorado, USA, June 2026. [preprint (arxiv: 2604.04913)] [project website] [code]

Frequency-Aware Flow Matching for High-Quality Image Generation Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, Liang-Chieh Chen In Conference on Computer Vision and Pattern Recognition (CVPR), Denver, Colorado, USA, June 2026. [preprint (arxiv: 2604.15521)] [code]