Liang-Chieh (Jay) Chen- Home Page

Liang-Chieh (Jay) Chen
Senior Principal Scientist, Amazon Frontier AI & Robotics (FAR), Los Angeles

Email: lcchen at cs dot ucla dot edu

About Me

I am currently a Senior Principal Scientist at Amazon Frontier AI & Robotics (FAR), where we are building cutting-edge robotics foundation models. We are a passionate team working at the intersection of computer vision, deep learning, and robotics. If you are interested in full-time roles or internships, feel free to reach out!

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 Research Scientist and Manager at ByteDance/TikTok (2023-2025), and before 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.

Activities

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

Action Editor for TMLR.

Reviewer for CVPR, ECCV, ICCV, NeurIPS.

Selected Recent Publications

Deeply Supervised Flow-Based Generative Models
Inkyu Shin, Chenglin Yang, Liang-Chieh Chen
Technical report
[preprint (arxiv: 2503.14494)] [project website]

FlowTok: Flowing Seamlessly Across Text and Image Tokens
Ju He, Qihang Yu, Qihao Liu, Liang-Chieh Chen
Technical report
[preprint (arxiv: 2503.10772)] [project website] [code]

Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation
Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, Liang-Chieh Chen
Technical report
[preprint (arxiv: 2502.20388)] [project website] [code]

COCONut-PanCap: Joint Panoptic Segmentation and Grounded Captions for Fine-Grained Understanding and Generation
Xueqing Deng, Qihang Yu, Ali Athar, Chenglin Yang, Linjie Yang, Xiaojie Jin, Xiaohui Shen, Liang-Chieh Chen
Technical report
[preprint (arxiv: 2502.02589)] [project website]

Democratizing Text-to-Image Masked Generative Models with Compact Text-Aware One-Dimensional Tokens
Dongwon Kim*, Ju He*, Qihang Yu*, Chenglin Yang, Xiaohui Shen, Suha Kwak, Liang-Chieh Chen
(*equal contribution)
Technical report
[preprint (arxiv: 2501.07730)] [project website] [code]

Randomized Autoregressive Visual Generation
Qihang Yu, Ju He, Xueqing Deng, Xiaohui Shen, Liang-Chieh Chen
Technical report
[preprint (arxiv: 2411.00776)] [project website] [code]

FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching
Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen, Alan Yuille, Liang-Chieh Chen
International Conference on Machine Learning (ICML), Vancouver, Canada, July 2025.
[preprint (arxiv: 2412.15205)] [code]

ViCaS: A Dataset for Combining Holistic and Pixel-level Video Understanding using Captions with Grounded Segmentation
Ali Athar, Xueqing Deng, Liang-Chieh Chen
In Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, Tennessee, USA, June 2025.
[preprint (arxiv: 2412.09754)] [project website] [code and dataset] [dataset (Hugging Face)]