Hao Dong Assistant Professor

+86 (0)10 6275-6561


Room 106-2, Courtyard No.5, Jingyuan

Generative Models, Computer Vision



Hao Dong is an assistant professor in CFCS-EECS at Peking University. He obtained a Ph.D. degree from Imperial College London under the supervision of Yike Guo in Fall 2019. His research involves deep learning and computer vision with the goal of reducing the data required for learning intelligent systems. He is passionate about popularizing artificial intelligence technologies and established TensorLayer, a deep learning and reinforcement learning library for scientists and engineers, which won the Best Open Source Software Award at ACM Multimedia 2017. Before Ph.D., he received a MSc specialist degree with distinction from Imperial, and a first-class BEng degree from the University of Central Lancashire. He founded a start-up for digital healthcare with Yike Guo between 2012 and 2014.




H. Dong, Y. Guo, G. Yang, “Deep Learning using TensorLayer (深度学习:一起玩转 TensorLayer)”, Publishing House of Electronics Industry. ISBN:9787121326226. 2018.

• A. Supratak, C. Wu, H. Dong, K. Sun and Y. Guo, “Survey on Feature Extraction and Applications of Biosignals. Machine Learning for Health Informatics”, Springer International Publishing, 161-182. 2016.




H. Dong, Z. Wei, C. Wu, Y. Guo, “Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security”, IEEE Trans. Information Forensics and Security (TIFS) IF:5.82. 2017.

• G. Yang, S. Yu, H. Dong, G. Slabaugh, P.L. Dragotti, X. Ye, F. Liu, S. Arridge, J. Keegan, Y. Guo, D. Firmin, “DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction”, IEEE Trans. Medical Imaging (TMI) IF:6.13. 2017.

H. Dong, A. Supratak, W. Pan, P.M. Matthews, Y. Guo, “Mixed Neural Network Approach for Sleep Stage Classification”, IEEE Trans. on Neural Systems and Rehabilitation Engineering (TNSRE) IF:3.97. 2017.

• A. Supratak, H. Dong, C. Wu, Y. Guo, “DeepSleepNet: A Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG”, IEEE Trans. on Neural Systems and Rehabilitation Engineering (TNSRE) IF:3.97. 2017.




H. Dong, S. Yu, C. Wu, Y. Guo, “Semantic Image Synthesis via Adversarial Learning”, Int. Conf. on Computer Vision (ICCV). 2017.

H. Dong, A. Supratak, L. Mai, F. Liu, A. Oehmichen, S. Yu, Y. Guo, “TensorLayer: A Versatile Library for Efficient Deep Learning Development”, ACM Multimedia (ACMMM). 2017.

H. Dong, J. Qing, D. McIlwraith, Y. Guo, “I2T2I: Learning Text to Image Synthesis with Textural Data Augmentation”, Int. Conf. on Image Processing (ICIP). 2017. (Oral)

H. Dong, F. Liu, Y. Mo, G. Yang, Y. Guo, “Automatic Brain Tumor Detection and Segmentation using U-Net Based Fully Convolutional Networks”, Medical Image Understanding and Analysis (MIUA). 2017. (Oral)

• S. Yu, H. Dong, P. Wang, C. Wu, Y. Guo, “Generative Creativity: Adversarial Learning for Bionic Design”, Neural Inform. Process. System (NIPS-W) Workshop. 2018.

• A. Supratak, S. Schneider. H. Dong, L. Li, Y. Guo, “Towards Desynchronization Detection in Biosignals”, Neural Inform. Process. System (NIPS-W) Time Series Workshop. 2017.

H. Dong, P.M. Matthews, Y. Guo, “A New Soft Material Based In-The-Ear EEG Recording Technique”, IEEE Engineering in Medicine and Biology Society (EMBC). 2016. (Oral)

• S. Zhang, H. Dong, W. Hu, Y. Guo, C. Wu, D. Xie, F. Wu, “Text-to-Image Synthesis via Visual-Memory Creative Adversarial Network”, Pacific-Rim Conf. on Multimedia (PCM), 2018.

• F. Liu, A. Oehmichen, J. Zhang, K. Sun, H. Dong, Y. Mo, Y. Guo, “TensorDB: Database Infrastructure for Continuous Machine Learning”, Int. Conf. Artificial Intelligence (ICAI). 2017.

• Z. Yao, H. Dong, F. Liu, Y. Guo, “Conditional Image Synthesis Using Stacked Auxiliary Classifier Generative Adversarial Networks”, Future of Information and Communication Conference (FICC). 2018.


Technical Reports


• S. Yu, H. Dong, G. Yang, G Slabaugh, P.L. Dragotti, X. Ye, F. Liu, S. Arridge, J. Keegan, D. Firmin, Y. Guo, “Deep De-Aliasing for Fast Compressive Sensing MRI”, arXiv:1705.07137. 2017.

H. Dong, P. Neekhara, C. Wu, Y. Guo, “Unsupervised Image-to-Image Translation with Generative Adversarial Networks”, arXiv:1701.02676. 2017.

• W. Pan, H. Dong, Y. Guo, “DropNeuron: Simplifying the Structure of Deep Neural Networks”, arXiv:1606.07326. 2016.

Research Lab



Hyperplane Lab



The primary research interests are in the fields of Deep/Machine Learning and Computer Vision, with broader interests in Digital Healthcare and Robotics. Our goal is to reduce the data required for learning intelligent systems. The current topics include:


• Unsupervised World Modelling: learning the representation of the world
• Generative + Reinforcement Learning: learning to interact with the world
• Generative + Computer Vision: learning to see the world