Research output per year
Research output per year
Personal profile
Chinese Name
殷可經
Biography
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
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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Dive into the research topics where Kejing YIN is active. Topic labels come from the works of this scholar.
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Learning Inter-Modal Correspondence and Phenotypes from Multi-Modal Electronic Health Records
Yin, K., Cheung, K. W., Fung, B. C. M. & Poon, J., 1 Sept 2022, In: IEEE Transactions on Knowledge and Data Engineering. 34, 9, p. 4328-4341 14 p.Research output: Contribution to journal › Article › peer-review
2 Citations (Scopus) -
SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors
Afshar, A., Yin, K., Yan, S., Qian, C., Ho, J., Park, H. & Sun, J., 18 May 2021, 35th AAAI Conference on Artificial Intelligence, AAAI 2021. Association for the Advancement of Artificial Intelligence, p. 6548-6556 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 35, no. 8)(AAAI-21/ IAAI-21/ EAAI-21 Proceedings).Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review
Open Access3 Citations (Scopus) -
TedPar: Temporally Dependent PARAFAC2 Factorization for Phenotype-based Disease Progression Modeling
Yin, K., Cheung, W. K., Fung, B. C. M. & Poon, J., 29 Apr 2021, Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). Demeniconi, C. & Davidson, I. (eds.). Society for Industrial and Applied Mathematics (SIAM), p. 594-602 9 p. (SIAM International Conference on Data Mining (SDM)).Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review
Open Access6 Citations (Scopus) -
Context-Aware Time Series Imputation for Multi-Analyte Clinical Data
Yin, K., Feng, L. & Cheung, K. W., Dec 2020, In: Journal of Healthcare Informatics Research. 4, 4, p. 411-426 16 p.Research output: Contribution to journal › Article › peer-review
2 Citations (Scopus) -
LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values
Yin, K., Afshar, A., Ho, J. C., CHEUNG, K. W., Zhang, C. & Sun, J., 23 Aug 2020, KDD 2020 - Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), p. 1625-1635 11 p. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).Research output: Chapter in book/report/conference proceeding › Conference contribution › peer-review
Open Access12 Citations (Scopus)