Assistant Professor
Department of Computer Science
Princeton University
Email: zhuangl@princeton.edu
35 Olden St, Princeton, NJ 08540
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Zhuang Liu
[Pronouncing Zhuang]
I'm an Assistant Professor of Computer Science at Princeton University. I received my Ph.D. in Computer Science from UC Berkeley, advised by Trevor Darrell, and B.S. in Computer Science from Yao Class, Tsinghua University. Before joining Princeton, I worked as a Research Scientist at Meta FAIR, New York. I also worked as a research intern at Cornell, Intel, Adobe, and FAIR.
My primary research areas are deep learning and computer vision. I work on deep learning model architectures, training, efficiency, and understanding. I'm also interested in (vision-) language models, and datasets in learning.
I seek to understand the workings of deep learning, and like to explore simple approaches to gain empirical insights into neural networks, their learning, and behaviors. My research often challenges existing beliefs, e.g., in architectures, training, pruning, and datasets.
I led the development of DenseNet (CVPR Best Paper Award) and ConvNeXt.
Research intern / visiting positions: please fill out this form to get in touch.
Prospective postdoc researchers (2026): please email me with "Prospective Postdoc" in the subject if interested.
Recent and selected publications (* equal contribution)
Transformers without Normalization
Jiachen Zhu, Xinlei Chen, Kaiming He, Yann LeCun, Zhuang Liu
CVPR 2025
Idiosyncrasies in Large Language Models
Mingjie Sun*, Yida Yin*, Zhiqiu Xu, J. Zico Kolter, Zhuang Liu
arXiv 2025
MetaMorph: Multimodal Understanding and Generation via Instruction Tuning
Shengbang Tong, David Fan, Jiachen Zhu, Yunyang Xiong, Xinlei Chen, Koustuv Sinha, Michael Rabbat,
Yann LeCun, Saining Xie, Zhuang Liu
arXiv 2024
Deconstructing Denoising Diffusion Models for Self-Supervised Learning
Xinlei Chen, Zhuang Liu, Saining Xie, Kaiming He
ICLR 2025
Understanding Bias in Large-Scale Visual Datasets
Boya Zeng*, Yida Yin*, Zhuang Liu
NeurIPS 2024
[Paper] [Video] [Code] [Project Page]
Rethinking the Value of Network Pruning
Zhuang Liu*, Mingjie Sun*, Tinghui Zhou, Gao Huang, Trevor Darrell
ICLR 2019
NeurIPS'18 Compact Neural Networks Workshop Best Paper Award
Learning Efficient Convolutional Networks through Network Slimming
Zhuang Liu, Jianguo Li, Zhiqiang Shen, Gao Huang, Shoumeng Yan, Changshui Zhang
ICCV 2017
Previous Publications (* equal contribution)
Anytime Dense Prediction with Confidence Adaptivity
Zhuang Liu, Hung-Ju Wang, Zhiqiu Xu, Trevor Darrell, Evan Shelhamer
ICLR 2022
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control
Zhuang Liu*, Xuanlin Li*, Bingyi Kang, Trevor Darrell
ICLR 2021
[Paper] [Code] [OpenReview] [Video]
Spotlight Presentation
