Gao Huang


Bio

I am an Associate Professor in the Department of Automation at Tsinghua University, where I lead the LEarning And Perception (LEAP) Lab. Previously, I was a postdoctoral researcher in the Department of Computer Science at Cornell University, working with Prof. Kilian Q. Weinberger. I received my PhD in machine learning from Tsinghua University in 2015.

My research focuses on the architectures and learning algorithms of foundation models, including large language models (LLMs), vision-language models (VLMs), and foundation models for embodied intelligence, such as vision-language-action models (VLAs) and world-action models (WAMs).

My research has been recognized with the Asian Young Scientist Fellowship (2024), the MIT Innovators Under 35 Award (2021), the DAMO Qingcheng Award (2020), and several other honors from academia and industry in China. I was also named an AI2000 Most Influential Scholar in Computer Vision. Our work has received the Best Paper Award at CVPR 2017 (DenseNet), the Outstanding Paper Award at ICML 2026 (JustGRPO), and the Best Paper Runner-Up Award at NeurIPS 2025 (Limit of RLVR). It was also selected as a Best Paper Finalist at CVPR 2026 (ViT³) and CVPR 2022 (Deformable Attention Transformer). Collectively, our publications have received more than 110,000 citations according to Google Scholar. (C.V.)


For students who are interested in joining our lab (PhD/Master/Intern/Postdoc), please contact me via gaohuang AT tsinghua.edu.cn.


Professional Activities

  • Associate Editor, IEEE Transactions on Pattern Analysis and Machine Intelligence (2023-).
  • Associate Editor, IEEE Transactions on Big Data (2021-).
  • Associate Editor, Pattern Recognition (2022-).
  • Area Chair of NeurIPS(2025, 2023, 2022), CVPR(2026, 2022, 2021), ICCV(2025, 2023), ICML(2022), UAI(2022).
  • Senior Program Committee (SPC) member of AAAI (2018, 2020), IJCAI (2021).
  • Reviewer for JMLR, TPAMI, IJCV, Machine Learning, IJCV, TIP, TKDE, TNNLS, ...
  • Reviewer for NeurIPS, ICML, CVPR, ICCV, ECCV, AAAI, AISTATS, ...

Awards

  • ICML Outstanding Paper Award, 2026
  • CVPR Best Paper Finalists, 2026
  • NeurIPS Best Paper Runner-up Award, 2025
  • Asian Young Scientist Fellowship, 2024
  • CVPR Best Paper Finalists, 2022
  • AI2000 Most Influential Scholar in Computer Vision, 2022
  • MIT TR 35 Asia-Pacific, MIT Technology Review, 2021
  • Research Fund for Outstanding Young Scholars, Nature Science Foundation of China, 2020
  • DAMO Qingcheng Award, Alibaba, 2020
  • Super AI Leader - Pioneer Award, World AI Conference (WAIC), 2018
  • CVPR Best Paper Award, 2017
Selected Conference Publications (Full Publication List on Google Scholar)

* Equal Contribution.

The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models. [project page]
Zanlin Ni, Shenzhi Wang, Yang Yue, Tianyu Yu, Weilin Zhao, Yeguo Hua, Tianyi Chen, Jun Song, Cheng Yu, Bo Zheng, Gao Huang.
International Conference on Machine Learning (ICML Outstanding Paper Award) 2026.
ViT3: Unlocking Test-Time Training in Vision. [code]
Dongchen Han, Yining Li, Tianyu Li, Zixuan Cao, Ziming Wang, Jun Song, Yu Cheng, Bo Zheng, Gao Huang.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR Best Paper Finalist) 2026.
Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model? [project page]
Yang Yue*, Zhiqi Chen*, Rui Lu, Andrew Zhao, Zhaokai Wang, Shiji Song, Gao Huang.
Neural Information Processing Systems (NeurIPS Best Paper Runner-up Award) 2025.
International Conference on Machine Learning (ICML) Workshop on AI4Math (Best Paper Award) 2025.
Differential Transformer. [code]
Tianzhu Ye, Li Dong, Yuqing Xia, Yutao Sun, Yi Zhu, Gao Huang, Furu Wei.
International Conference on Learning Representations (ICLR Oral) 2025.
Agent Attention: On the Integration of Softmax and Linear Attention. [code]
Dongchen Han*, Tianzhu Ye*, Yizeng Han, Zhuofan Xia, Shiji Song, Gao Huang.
European Conference on Computer Vision (ECCV) 2024.

ExpeL: LLM Agents Are Experiential Learners. [code]
Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, Gao Huang.
AAAI Conference on Artificial Intelligence (AAAI Oral) 2024.

FLatten Transformer: Vision Transformer using Focused Linear Attention. [code]
Dongchen Han*, Xuran Pan*, Yizeng Han, Shiji Song, Gao Huang.
International Conference on Computer Vision (ICCV) 2023.
On the Integration of Self-Attention and Convolution. [code]
Xuran Pan, Chunjiang Ge, Rui Lu, Shiji Song, Guanfu Chen, Zeyi Huang, Gao Huang.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
Vision Transformer with Deformable Attention. [code]
Zhuofan Xia*, Xuran Pan*, Shiji Song, Li Erran Li, Gao Huang.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR Best Paper Finalist) 2022.
Adaptive Focus for Efficient Video Recognition. [code]
Yulin Wang*, Zhaoxi Chen*, Haojun Jiang, Shiji Song, Yizeng Han, Gao Huang.
International Conference on Computer Vision (ICCV Oral) 2021.

3D Object Detection with Pointformer. [code]
Xuran Pan*, Zhuofan Xia*, Shiji Song, Li Erran Li, Gao Huang.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation. [code]
Zhenda Xie, Zheng Zhang, Xizhou Zhu, Gao Huang , Stephen Lin.
European Conference on Computer Vision (ECCV Oral) 2020.
Asymmetric Valleys: Beyond Sharp and Flat Local Minima. [code] [slides]
Haowei He, Gao Huang, Yang Yuan.
Neural Information Processing Systems (NeurIPS Spotlight) 2019.
Rethinking the Value of Network Pruning. [code]
Zhuang Liu*, Mingjie Sun*, Tinghui, Zhou, Gao Huang, Trevor Darrell.
International Conference on Learning Representations (ICLR) 2019.
CondenseNet: An Efficient DenseNet using Learned Group Convolutions. [code] [talk]
Gao Huang*, Shichen Liu*, Laurens van der Maaten, Kilian Q. Weinberger.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR Spotlight) 2018.

Multi-Scale Dense Convolutional Networks for Resource Efficient Image Classification. [code]
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, Kilian Q. Weinberger.
International Conference on Learning Representations (ICLR Oral) 2018.


Densely Connected Convolutional Networks. [code] [talk] [slides]
Gao Huang*, Zhuang Liu*, Laurens van der Maaten, Kilian Weinberger.
IEEE Conference on Computer Vision and Pattern Recognition (CVPR Best Paper Award) 2017.

Snapshot Ensembles: Train 1, Get M for Free. [code]
Gao Huang*, Yixuan Li*, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Weinberger.
International Conference on Learning Representations (ICLR) 2017.

Deep Networks with Stochastic Depth. [code] [poster] [talk]
Gao Huang*, Yu Sun*, Zhuang Liu, Daniel Sedra, Kilian Weinberger.
European Conference on Computer Vision (ECCV Spotlight) 2016.

(This paper was recommended as an Oral at NIPS 2016 Deep Learning Symposium)



Selected Journal Papers (Full Publication List on Google Scholar)

* Equal Contribution.

Emulating Human-like Adaptive Vision for Efficient and Flexible Machine Visual Perception. [code]
Yulin Wang*, Yang Yue*, Yang Yue*, Huanqian Wang, Haojun Jiang, Yizeng Han, Zanlin Ni, Yifan Pu, Minglei Shi, Rui Lu, Qisen Yang, Andrew Zhao, Zhuofan Xia, Shiji Song, Gao Huang.
Nature Machine Intelligence (NMI), 2025.
Towards Expert-level Autonomous Carotid Ultrasonography with Large-scale Learning-based Robotic System. [code]
Haojun Jiang*, Andrew Zhao*, Qian Yang*, Xiangjie Yan, Teng Wang, Yulin Wang, Ning Jia, Jiangshan Wang, Guokun Wu, Yang Yue, Shaqi Luo, Huanqian Wang, Ling Ren, Siming Chen, Pan Liu, Guocai Yao, Wenming Yang, Shiji Song, Xiang Li, Kunlun He, Gao Huang.
Nature Communications (NC), 2025.
AdaGen: Learning Adaptive Policy for Image Synthesis. [code]
Zanlin Ni*, Yulin Wang*, Yeguo Hua, Renping Zhou, Jiayi Guo, Jun Song, Bo Zheng, Gao Huang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2025.
Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition. [code]
Yulin Wang*, Haoji Zhang*, Yang Yue, Shiji Song, Chao Deng, Junlan Feng, Gao Huang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2025.
EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training. [code]
Yulin Wang, Yang Yue, Rui Lu, Yizeng Han, Shiji Song, Gao Huang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2024.
Latency-aware Unified Dynamic Networks for Efficient Image Recognition. [code]
Yizeng Han*, Zeyu Liu*, Zhihang Yuan*, Yifan Pu, Chaofei Wang, Shiji Song, Gao Huang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2024.
Dynamic Neural Networks: Advantages and Challenges.
Gao Huang.
National Science Review (NSR), 2024.
Probabilistic Contrastive Learning for Long-Tailed Visual Recognition. [code]
Chaoqun Du, Yulin Wang, Shiji Song, Gao Huang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2024.
Glance and Focus Networks for Dynamic Visual Recognition. [code]
Gao Huang*, Yulin Wang*, Kangchen Lv, Haojun Jiang, Wenhui Huang, Pengfei Qi, Shiji Song.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2022.
Dynamic Neural Networks: A Survey.
Yizeng Han*,Gao Huang*, Shiji Song, Le Yang, Honghui Wang, Yulin Wang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2021.
Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning.
Wenjie Shi, Gao Huang, Shiji Song, Cheng Wu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2021.
Regularizing Deep Networks with Semantic Data Augmentation. [code]
Yulin Wang*, Gao Huang*, Shiji Song, Xuran Pan, Yitong Xia, Cheng Wu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2021.
Self-Supervised Discovering of Interpretable Features for Reinforcement Learning. [code]
Wenjie Shi, Gao Huang, Shiji Song, Zhuoyuan Wang, Tingyu Lin, Cheng Wu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI), 2020.
Convolutional Networks with Dense Connectivity. [code]
Gao Huang, Zhuang Liu, Geoff Pleiss, Laurens Van Der Maaten, Kilian Q. Weingerger.
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE TPAMI) 2019.

(Journal version of DenseNet; Deep understanding of dense connectivity.)





Students

Contact

  • gaohuang at tsinghua dot edu dot cn
  • 617A Centre Main Building, Tsinghua University, Beijing 100084, China.