Kejing Yin is a Research Assistant Professor at the Department of Computer Science at Hong Kong Baptist University. Prior to that, he was a Post-doctoral Research Fellow under the supervision of Prof. William K. Cheung. Kejing obtained the Ph.D. degree from the same department and the Bachelor’s degree from the School of Electric Power at South China University of Technology. He was a visiting PhD student at the College of Computing at Georgia Institute of Technology, supervised by Prof. Jimeng Sun, from Sep. 2019 to Feb. 2020.
He is working on machine learning for high-dimensional healthcare data analytics. He is particularly interested in computational phenotyping and predictive analytics for large-scale electronic health records (EHR) data. He serves as a program committee member or reviewer for international conferences, including AAAI, IJCAI, NeurIPS, ICLR, and ICHI.
Ph.D., 2021
Hong Kong Baptist University
B.Eng., 2015
South China University of Technology
| [TKDE] | Kejing Yin, Dong Qian, William K. Cheung. PATNet: Propensity-Adjusted Temporal Network for Joint Imputation and Prediction using Binary EHRs with Observation Bias. IEEE Transactions on Knowledge and Data Engineering, 2023. |
| [TIST] | Chin Wang Cheong, Kejing Yin, William K. Cheung, Benjamin C. M. Fung, Jonathan Poon. Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding. ACM Transactions on Intelligent Systems and Technology, 2023. |
| [TKDE] | Kejing Yin, William K. Cheung, Benjamin C. M. Fung, Jonathan Poon. Learning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health Records. IEEE Transactions on Knowledge and Data Engineering, 2022. |
| [SDM-21] | Kejing Yin, William K. Cheung, Benjamin C. M. Fung, Jonathan Poon. TedPar: Temporally Dependent PARAFAC2 Factorization for Phenotype-based Disease Progression Modeling. Proceedings of the 2021 SIAM International Conference on Data Mining, 2021. |
| [AAAI-21] | Ardavan Afshar, Kejing Yin, Sherry Yan, Cheng Qian, Joyce C. Ho, Haesun Park, Jimeng Sun. SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors. Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021. |
| [KDD-20] | Kejing Yin, Ardavan Afshar, Joyce C. Ho, William K. Cheung, Chao Zhang, Jimeng Sun. LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020. |
| [IJCAI-19] | Lihong Song, Chin Wang Cheong, Kejing Yin, William K. Cheung, Benjamin C. M. Fung, Jonathan Poon. Medical Concept Embedding with Multiple Ontological Representations. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2019. |
| [AAAI-19] | Kejing Yin, Dong Qian, William K. Cheung, Benjamin C. M. Fung, Jonathan Poon. Learning Phenotypes and Dynamic Patient Representations via RNN Regularized Collective Non-negative Tensor Factorization. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019. |
| [IJCAI-18] | Kejing Yin, William K. Cheung, Yang Liu, Benjamin C. M. Fung, Jonathan Poon. Joint Learning of Phenotypes and Diagnosis-Medication Correspondence via Hidden Interaction Tensor Factorization. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018. |