About me
Jingqing Zhang (张敬卿) is the technical co-founder and Head of AI at Pangaea Data. His focus is to build an AI-driven product to characterize patients and improve patient outcomes. His team is hiring: openings. He is also a Research Associate at Data Science Institute, Imperial College London. Prior to this, he obtains his PhD degree in Computing from Imperial College London under the supervision of Prof. Yi-Ke Guo. He received his BEng degree in Computer Science and Technology from Tsinghua University. His research interest includes Natural Language Processing, Text Mining, Data Mining and Deep Learning, especially their applications in healthcare.
Activities
- [05/2022] I will present with my colleagues about our work to support oncologists to create research-quality genomic data from unstructured clinical notes with 97\% in a privacy preserving manner at Bio-IT 2022. more
- [03/2022] I will present with my colleagues about our work to support oncologists to find more cancer patients at risk of cachexia at HIMSS 2022. more
- [09/2021] Our paper about detection of contextual synonyms for phenotyping will publish at EMNLP 2021 paper. The ICU use case of the paper is also available on arxiv paper.
- [06/2020] PEGASUS, a pre-training model tailored for abstractive text summarization which achieves state-of-the-art on 12 datasets, is now public (including code and checkpoints). This work is completed together with my excellent colleagues during my internship at Google Research, Brain Team. PEGASUS is accepted by ICML 2020.
- [06/2020] Our book about Deep Reinforcement Learning by Springer is available for pre-order now.
- [04/2020] I was invited to share our research about clinical NLP at LexisNexis HPCC Tech Talk. Video
- [02/2019] I was invited to share our research about clinical NLP at Elsevier: talk and slides.
- [10/2019] Two papers accepted by BIBM 2019. One about unsupervised phenotype annotation on medical notes [more] and the other using VAE to extract low dimentional features from multi-omics data [more].
- [04/2019] TensorLayer 2.0 has been released! [Github] [Doc]
- [03/2019] Our paper about zero-shot text classification was presented as a talk in NAACL-HLT’19 [more].
- [12/2018] Together with Dr. Luo Mai, we gave a talk at GDG DevFest London 2018 about TensorLayer: video
- [10/2018] I gave a talk at the 2018 HPCC Systems Community Day: video and slides.
- [08/2018] Our papers about traffic prediction using online search query appeared in KDD’18 and ACMMM’18.
- [03/2018] Our Chinese Deep Learning book is now published: “Deep Learning Using TensorLayer” 《深度学习：一起玩转TensorLayer》[Press] [Github]
Publications
Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge
Ashwani Tanwar, Jingqing Zhang, Julia Ive, Vibhor Gupta, and Yike Guo. "Unsupervised Numerical Reasoning to Extract Phenotypes from Clinical Text by Leveraging External Knowledge." arXiv preprint arXiv:2204.10202 (2022).
Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case
Jingqing Zhang, Luis Bolanos Trujillo, Tong Li, Ashwani Tanwar, Guilherme Freire, Xian Yang, Julia Ive, Vibhor Gupta, and Yike Guo. 2021. Self-Supervised Detection of Contextual Synonyms in a Multi-Class Setting: Phenotype Annotation Use Case. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8754–8769, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases
Jingqing Zhang, Luis Bolanos, Ashwani Tanwar, Julia Ive, Vibhor Gupta, and Yike Guo. "Clinical Utility of the Automatic Phenotype Annotation in Unstructured Clinical Notes: ICU Use Cases." arXiv preprint arXiv:2107.11665 (2021).
Deep Reinforcement Learning: Fundamentals, Research and Applications
Hao Dong, Zihan Ding, Shanghang Zhang, Jingqing Zhang, et al., Deep Reinforcement Learning: Fundamentals, Research and Applications, Springer Nature, 2020
PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
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.
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
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.
Dest-ResNet: a Deep Spatiotemporal Residual Network for Hotspot Traffic Speed Prediction
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.
The Deep Poincare Map: A Novel Approach for Left Ventricle Segmentation
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.
Deep Sequence Learning with Auxiliary Information for Traffic Prediction
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.
深度学习：一起玩转TensorLayer (Deep Learning Using TensorLayer)
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
I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation
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.