I am an Applied Scientist at Amazon AWS AI. My research interests lie primarily in deep graph learning, NLP and machine learning using large-scale graph-structured and text datasets, with an emphasis on knowledge graphs, question answering, taxonomy construction, healthcare informatics, and drug discovery. Before joining Amazon, I was a Research Scientist at JD AI Research. I received my Ph.D. from the Computer Science and Engineering Department, University of Connecticut, advised by Prof. Jinbo Bi. I got my M.S. degree at Beijing University of Posts and Telecommunications (BUPT).

Experience

06/2022 - Present | Applied Scientist | Amazon AWS AI

  • Focus on the natural language processing (NLP) tasks.

09/2020 - 05/2022 | Research Scientist | JD AI Research, JD.COM Silicon Valley Research Center

  • Focus on the deep graph learning, NLP and machine learning with an emphasis on knowledge graphs and QA.
  • Mentor: Dr. Jing Huang

01/2020 - 06/2020 | Research Intern | MIT-IBM Watson AI Lab, IBM Research

  • Designed a method to explore the correlation and causation among the time series when structure information is unknown.
  • Mentor: Dr. Jie Chen

05/2019 - 09/2019 | Research Intern | IBM Thomas J. Watson Research Center, IBM Research

  • Proposed an end-to-end learning framework which utilizes cross-domain knowledge and graph structure to construct the taxonomy of missing domain.
  • Knowledge Induction Team @ IBM Research AI

05/2018 - 09/2018 | Research Intern | JD AI Research, JD.COM Silicon Valley Research Center

  • Proposed a novel end-to-end structure-aware convolutional network which incorporates graph connectivity structure seamlessly into a new convolutional translating embedding model for knowledge graph completion.
  • SAIL-JD Knowledge Graph Research Program

09/2015 - 08/2020 | Research Assistant | University of Connecticut

  • Designed graph neural networks, NLP, machine learning methods to improve drug discovery, knowledge graph completion, depression detection and so on.

07/2014 - 11/2014 | Research Assistant | University of Southern California

  • Developed effective knowledge discovery and data mining techniques for emerging unstructured data.

Publications

Variance of the Gradient Also Matters: Privacy Leakage from Gradients
Yijue Wang, Jieren Deng, Dan Guo, Chenghong Wang, Xianrui Meng, Hang Liu, Chao Shang, Binghui Wang, Qin Cao, Caiwen Ding, Sanguthevar Rajasekaran. 2022 IEEE World Congress on Computational Intelligence, 2022.

Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs
Chao Shang, Guangtao Wang, Peng Qi, Jing Huang. The 60th Annual Meeting of the Association for Computational Linguistics (ACL), 2022.

TAG: Gradient Attack on Transformer-based Language Models
Jieren Deng, Yijue Wang, Ji Li, Chenghong Wang, Chao Shang, Hang Liu, Sanguthevar Rajasekaran and Caiwen Ding. The 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021.

Open Temporal Relation Extraction for Question Answering
Chao Shang, Peng Qi, Guangtao Wang, Jing Huang, Youzheng Wu, and Bowen Zhou. Automated Knowledge Base Construction (AKBC), 2021.

Discrete Graph Structure Learning for Forecasting Multiple Time Series
Chao Shang, Jie Chen, and Jinbo Bi. The International Conference on Learning Representations (ICLR), 2021. Code

Multi-view spectral graph convolution with consistent edge attention for molecular modeling
Chao Shang, Qinqing Liu, Qianqian Tong, Jiangwen Sun, Minghu Song, and Jinbo Bi. Neurocomputing, 2021. Code

End-to-End Structure-Aware Convolutional Networks on Graphs
Chao Shang. University of Connecticut, 2020.

Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer
Chao Shang, Sarthak Dash, Md Faisal Mahbub Chowdhury, Nandana Mihindukulasooriya, and Alfio Gliozzo. The 58th Annual Meeting of the Association for Computational Linguistics (ACL), 2020. Code

Automatic depression prediction using Internet traffic characteristics on smartphones
Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis, Bing Wang. Smart Health, 2020.

Predicting Outcomes of Chemical Reactions: A Seq2Seq Approach with Multi-view Attention and Edge Embedding
Xia Xiao, Chao Shang, Jinbo Bi and Sanguthevar Rajasekaran. International Joint Conference on Neural Networks (IJCNN), 2020.

End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
Chao Shang, Yun Tang, Jing Huang, Jinbo Bi, Xiaodong He, and Bowen Zhou. The AAAI Conference on Artificial Intelligence (AAAI), 2019. Code

Large-scale Automatic Depression Screening Using Meta-data from WiFi Infrastructure
Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis and Bing Wang. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2019. (ACM Journal of IMWUT)

Predicting Depressive Symptoms Using Smartphone Data
Shweta Ware, Chaoqun Yue, Reynaldo Morillo, Jin Lu, Chao Shang, Jayesh Kamath, Athanasios Bamis, Jinbo Bi, Alexander Russell and Bing Wang. IEEE/ACM CHASE, 2019.

Edge Attention-based Multi-Relational Graph Convolutional Networks
Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi, and Jinbo Bi. arXiv preprint arXiv:1802.04944, 2018.

Joint Modeling of Heterogeneous Sensing Data for Depression Assessment via Multi-task Learning
Jin Lu, Chao Shang, Chaoqun Yue, Reynaldo Morillo, and Shweta Ware, Jayesh Kamath, Athansios Bamis, Alexander Russell, Bing Wang, and Jinbo Bi. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2018. (ACM Journal of IMWUT)

Fusing Location Data for Depression Prediction
Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis and Bing Wang. IEEE Transactions on Big Data (IEEE TBDATA), 2018.

VIGAN: Missing View Imputation with Generative Adversarial Networks
Chao Shang, Aaron Palmer, Jiangwen Sun, Ko-Shin Chen, Jin Lu, Jinbo Bi.
IEEE International Conference on Big Data (IEEE BigData), 2017. Code

Fusing Location Data for Depression Prediction
Chaoqun Yue, Shweta Ware, Reynaldo Morillo, Jin Lu, Chao Shang, Jinbo Bi, Jayesh Kamath, Alexander Russell, Athanasios Bamis and Bing Wang. IEEE Ubiquitous Intelligence & Computing (UIC), 2017.

Event Extraction from Unstructured Text Data
Chao Shang, Anand Panangadan, and Victor K. Prasanna.
International Conference on Database and Expert Systems Applications (DEXA), 2015.

A carrier class IoT service architecture integrating IMS with SWE
Dongliang Xie, Chao Shang, Jinchao Chen, Yongfang Lai, and Chuanxiao Pang.
International Journal of Distributed Sensor Networks (IJDSN), 2014 .

Professional Services

Program Committee / Reviewer

  • Program Committee Member of AAAI 2023.
  • Program Committee Member of EMNLP 2022, IJCAI 2022, ACL 2022, AAAI 2022.
  • Program Committee Member of ACL/IJCNLP 2021, NAACL 2021, EMNLP 2021, IJCAI 2021, AAAI 2021, EACL 2021.
  • Program Committee Member of EMNLP 2020, ACL 2020, IJCAI 2020, CIKM 2020.
  • Program Committee Member of CIKM 2019, ECML-PKDD 2019, IJCAI 2019.
  • Reviewer of ACL Rolling Review.
  • Reviewer of IEEE Transactions on Neural Networks and Learning Systems (TNNLS).
  • Reviewer of Transactions on Audio, Speech, and Language Processing.
  • Reviewer of Artificial Intelligence Review.
  • Reviewer of Journal of Artificial Intelligence Research (JAIR).
  • Reviewer of Journal of Chemical Information and Modeling (JCIM).
  • Reviewer of IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB).
  • Reviewer of American Medical Informatics Association (AMIA) Clinical Informatics Conference.
  • Reviewer of Journal of Health Information Science and Systems.
  • Reviewer of ACS Omega.
  • Reviewer of PLOS ONE journal.
  • External Reviewer: KDD’19; KDD’18; AAAI’18 and so on.

Session Chair

  • SR1: Graph Neural Networks, CIKM 2019.

Awards

  • 2020 Departmental Research Excellence Award
  • 2019 Predoctoral Prize for Research Excellence
  • 2019 AAAI Student Scholarship.
  • 2018 Predoctoral Honorable Mention Award.
  • 2018 the 4th Annual Graduate Poster Competition, Computer Science & Engineering First Place Award.
  • 2017 IEEE International Conference on Big Data, Student Travel Award.
  • 2014 National Scholarship for Graduate Students.

Teaching Experience

  • CSE-1010 Introduction to Computing for Engineers (09/2016-05/2017)
  • CSE-5820 Machine Learning (01/2017-05/2017)

Contact

Email: shangchaocs AT gmail.com