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Yang Gao

PI(July 2020 to present)
Special-term Research Fellow、Assistant Professor


Gao Yang is an Assistant Professor at Institute for Interdisciplinary Information Science (IIIS) at Tsinghua University.  

 He obtained his Ph.D. degree from UC Berkeley, advised by Prof. Trevor Darrell and B.E. from the Computer Science Department at Tsinghua University.

He is interested in the intersection between computer vision and robotics. Specifically, He wants to explore how to utilize the prior knowledge we have from computer vision to do robot manipulation tasks both more efficiently and effectively. This not only involves understanding how to use the previous visual experiences but also potentially needs re-designing robotic learning algorithms to better handles the visual states. Thus, it is a co-design problem between vision and robotics. As testing benchmarks, he works on robot manipulation and autonomous driving applications, both in simulation and in the real world. 

Personal honor: 

Beijing Talented Youth Program


Computer Vision 

Reinforcement learning


AI-Robust autonomous driving project 

Embodied AI and embodied foundation model



Open positions

Research Direction:

Reinforcement learning

Computer vision



1. Responsible for theoretical research, algorithm or system development in the above-mentioned related fields and directions;

2. Publish academic or innovative research results in the above-mentioned related fields.


1. Computer, electronics, automation, software and other related professional background, strong academic ability;

2. Possess excellent basic theoretical knowledge and programming skills in the field (Python, Linux, C++, etc.);

3. Those who have published papers in top conferences in the above fields are preferred

Please send your CV:



1. Jinkun Cao, Ruiqian Nai, Qing Yang, Jialei Huang, Yang Gao,An Empirical Study on Disentanglement of Negative-free Contrastive Learning, 2022 Neural Information Processing Systems (NeurIPS), 2022

2. Zhao-Heng Yin, Weirui Ye, Qifeng Chen, Yang Gao,Planning for Sample Efficient Imitation Learning,2022 Neural Information Processing Systems (NeurIPS), 2022.

3. Weirui Ye, Pieter Abbeel, Yang Gao,Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions,2022 Neural Information Processing Systems (NeurIPS), 2022

4. Renhao Wang, Hang Zhao, Yang Gao,CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation, 2022 European Conference on Computer Vision (ECCV), 2022

5. Chenyu Yang, Wanrong He, Yingqing Xu, and Yang Gao,EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer, 2022European Conference on Computer Vision (ECCV), 2022.

6. Yingdong Hu, Renhao Wang, Kaifeng Zhang, Yang Gao,Semantic-Aware Fine-Grained Correspondence,2022 European Conference on Computer Vision (ECCV), 2022.

7. Chia-Chi Chuang, Donglin Yang, Chuan Wen, Yang Gao,Resolving Copycat Problems in Visual Imitation Learning via Residual Action Prediction,2022 European Conference on Computer Vision (ECCV), 2022.

8. Chuan Wen*, Jierui Lin*, Jianing Qian, Yang Gao, Dinesh Jayaraman Keyframe-Focused Visual Imitation Learning. International Conference on Machine Learning (ICML) , 2021.

9. Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao Mastering Atari Games with Limited Data Advances. Neural Information Processing Systems (NeurIPS), 2021.

10. Chuan Wen*, Jierui Lin*, Trevor Darrell, Dinesh Jayaraman, Yang Gao Fighting Copycat Agents in Behavioral Cloning from Observation Histories Neural Information Processing Systems (NeurIPS), 2020.