Ching-Yao Chuang

Research Scientist
GenAI, Meta
cychuang _at_ meta _dot_ com


Curriculum vitæ / Google Scholar

I am a research scientist at Meta GenAI. Prior to that, I obtained my Ph.D. from MIT EECS where I was advised by Prof. Antonio Torralba and Prof. Stefanie Jegelka. I am interested in generative AI and core machine learning.

Research Highlights



Publications

Fairy: Fast Parallelized Instruction-Guided Video-to-Video Synthesis
Bichen Wu, Ching-Yao Chuang, Xiaoyan Wang, Yichen Jia, Kapil Krishnakumar, Tong Xiao, Feng Liang, Licheng Yu, Peter Vajda
CVPR 2024 [paper] [project]

Debiasing Vision-Language Models via Biased Prompts
Ching-Yao Chuang, Varun Jampani, Yuanzhen Li, Antonio Torralba, and Stefanie Jegelka
arXiv Preprint 2023 [paper] [code]

The Inductive Bias of Flatness Regularization for Deep Matrix Factorization
Khashayar Gatmiry, Zhiyuan Li, Ching-Yao Chuang, Sashank Reddi, Tengyu Ma, and Stefanie Jegelka
NeurIPS 2023 [paper]

InfoOT: Information Maximizing Optimal Transport
Ching-Yao Chuang, Stefanie Jegelka, and David Alvarez-Melis
ICML 2023 [paper] [code]

Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
Ching-Yao Chuang and Stefanie Jegelka
NeurIPS 2022 [paper] [code]

Robust Contrastive Learning against Noisy Views
Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, and Yale Song
CVPR 2022 [paper] [code]

Measuring Generalization with Optimal Transport
Ching-Yao Chuang, Youssef Mroueh, Kristjan Greenewald, Antonio Torralba, and Stefanie Jegelka
NeurIPS 2021 Spotlight [paper] [code]

Contrastive Learning with Hard Negative Samples
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka
ICLR 2021 [paper] [code]

Fair Mixup: Fairness via Interpolation
Ching-Yao Chuang, Youssef Mroueh
ICLR 2021 [paper] [code]

Debiased Contrastive Learning
Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen, Antonio Torralba, and Stefanie Jegelka
NeurIPS 2020 Spotlight [paper] [code]

Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
Ching-Yao Chuang, Antonio Torralba, and Stefanie Jegelka
ICML 2020 [paper] [project page]

The Role of Embedding Complexity in Domain-invariant Representations
Ching-Yao Chuang, Antonio Torralba, and Stefanie Jegelka
ICML 2019 AMTL Workshop [paper] [code]

Learning to Act Properly: Predicting and Explaining Affordances from Images
Ching-Yao Chuang, Jiaman Li, Antonio Torralba and Sanja Fidler
CVPR 2018 [paper] [project page]

Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner
Tseng-Hung Chen, Yuan-Hong Liao, Ching-Yao Chuang, Wan-Ting Hsu, Jianlong Fu and Min Sun
ICCV 2017 [paper] [project page]

Leveraging Video Descriptions to Learn Video Question Answering
Kuo-Hao Zeng, Tseng-Hung Chen, Ching-Yao Chuang, Yuan-Hong Liao, Juan Carlos Niebles and Min Sun
AAAI 2017 [paper] [project page] [dataset]

Work experience

Sep 2023 - Present Research Scientist Generative AI, Meta
May 2022 - Aug 2022 Research Intern Microsoft Research, New England
Jun 2021 - Aug 2021 Research Intern Microsoft Research, Redmond
Jul 2020 - Sep 2020 Research Intern IBM Research AI
Sep 2018 - Aug 2023 R.A. Computer Science and Artificial Intelligence Lab, MIT
Aug 2017 - Dec 2017 R.A. Machine Learning Lab, University of Toronto