About
Dr. Kejing Yin is a Research Assistant Professor in the Department of Computer Science, Hong Kong Baptist University. Before his RAP appointment, he served as a Post-doctoral Research Fellow in the same department. He received his Ph.D. degree from the Department of Computer Science, Hong Kong Baptist University in 2021, and his Bachelor’s degree from the South China University of Technology in 2015. He was a visiting Ph.D. student at the College of Computing at Georgia Institute of Technology from Sep. 2019 to Feb. 2020. His research interests mainly focus on machine learning for high-dimensional healthcare data analytics, including 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.
Research Interests
- Machine Learning for Healthcare
- Healthcare Data Analysis
- Computational Phenotyping
- Temporal Data Analysis
Selected Publications
- 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 (SDM-21), 2021.
- 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 (AAAI-21), 2021.
- 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 (IEEE TKDE), 2020.
- 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 (KDD-20), 2020.
- Kejing Yin, Liaoliao Feng, William K. Cheung. Context-Aware Time Series Imputation for Multi-Analyte Clinical Data. Journal of Healthcare Informatics Research, 2020.
- 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 (IJCAI-19), 2019.
- 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 (AAAI-19), 2019.
- 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 (IJCAI-18), 2018.