Hao Dong, Zihan Ding, Shanghang Zhang, Jingqing Zhang, et al., Deep Reinforcement Learning: Fundamentals, Research and Applications, Springer Nature, 2020
- PhD Student at Department of Computing, in Imperial College London, 2021 (Expected)
- M.Res. from Department of Computing, in Imperial College London, 2017 (Distinction)
- B.Eng. from Department of Computer Science and Technology, in Tsinghua University, 2016 (GPA 91/100)
- Research Intern @ Google Research, Brain Team, Mountain View, CA, USA. From Sep to Dec 2019. We propose PEGASUS, a pre-training model tailored for abstractive text summarization which achieves state-of-the-art on 12 datasets. Star
Jingqing Zhang, Yao Zhao, Mohammad Saleh, Peter J. Liu. "PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization." Thirty-seventh International Conference on Machine Learning (ICML). 2020.
Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification
Xiaoyu Zhang, Jingqing Zhang, Kai Sun, Xian Yang, Chengliang Dai, Yike Guo. "Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification". 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019.
Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records
Jingqing Zhang, Xiaoyu Zhang, Kai Sun, Xian Yang, Chengliang Dai, Yike Guo. "Unsupervised Annotation of Phenotypic Abnormalities via Semantic Latent Representations on Electronic Health Records". 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019.
Jingqing Zhang, Piyawat Lertvittayakumjorn, Yike Guo. "Integrating Semantic Knowledge to Tackle Zero-shot Text Classification". In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT), 2019.
Binbing Liao, Jingqing Zhang, Ming Cai, Siliang Tang, Yifan Gao, Chao Wu, Shengwen Yang, Wenwu Zhu, Yike Guo, Fei Wu. "Dest-ResNet: a Deep Spatiotemporal Residual Network for Hotspot Traffic Speed Prediction". In Proceedings of the 2018 ACM on Multimedia Conference. ACM, 2018.
Yuanhan Mo, Fangde Liu, Douglas McIlwraith, Guang Yang, Jingqing Zhang, Taigang He, and Yike Guo. "The Deep Poincare Map: A Novel Approach for Left Ventricle Segmentation". MICCAI 2018, The 21st International Conference on Medical Image Computing and Computer-Assisted Intervention. 2018.
Liao, Binbing, Jingqing Zhang, Chao Wu, Douglas McIlwraith, Tong Chen, Shengwen Yang, Yike Guo, and Fei Wu. "Deep Sequence Learning with Auxiliary Information for Traffic Prediction." In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2018.
董豪，郭毅可，杨光，张敬卿，于思淼，陈竑，林一鸣，莫元汉，袁航，幺忠玮，吴超，王剑虹 (Hao Dong, Yike Guo, Guang Yang et al), 深度学习：一起玩转TensorLayer (Deep Learning Using TensorLayer), 电子工业出版社 (Publishing House of Electronics Industry), 2018 ISBN: 9787121326226
Dong, Hao, Jingqing Zhang, Douglas McIlwraith, and Yike Guo. "I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation." Image Processing (ICIP), 2017 IEEE International Conference on. IEEE, 2017.
TensorLayer 2.0 Star
A novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. Best Open Source Software Award @ACM Multimedia (MM) 2017.
KDDCUP 2019 AutoML Track
A solution for the KDDCUP 2019 AutoML Track using LightGBM. [Poster]
Awards & Scholarships
- PhD Scholarship supported by LexisNexis HPCC Systems Academic Program 2016-2020
- National Scholarship of China (top 1%) in 2015
- Tsinghua & Tung OOCL Scholarship (top 10%) in 2014
- Tsinghua & Evergrand Scholarship (top 10%) in 2013
- Reviewer of workshops: ICCS19-MLDADS
- Reviewer of conferences: MICCAI
- Reviewer of journals: TKDE, Cognitive Systems Research
Graduate Teaching Assistant
- CO145 Mathematical Method: 2018 Fall
- CO202 Algorithm II: 2018 Spring, 2018 Fall
- CO245 Probability and Statistics: 2018 Spring, 2019 Spring, 2020 Spring
- CO395 Introduction to Machine Learning: 2019 Spring
- CO490 Natural Language Processing: 2020 Spring
- Data Science Camp: 2019 Winter, 2019 Summer