Publications

* denotes corresponding author.

2026

  1. Feilong Wu, Kejing Yin, and William K. Cheung, “When Clinical Notes Are Incomplete: Fusing Clinical Notes and Time Series Using Diffusion-based Models,” in The 2026 Conference on Empirical Methods in Natural Language Processing (EMNLP-26 Findings), 2026.
  2. Fan Wu, Danni Xu, Kejing Yin, Hao Wu, Huiling Zhang, Wenfang Yao, Shanyun He, Lina Zhang, Zhaoxin Qian, and Feng Lyu, “SADAER: Scalable Attention Diffusion Framework for Multi-task Abnormal EEG Recognition,” in IEEE International Conference on Data Mining (ICDM-26), 2026.
  3. Kexin Xie, Baoyao Yang*, Kejing Yin*, William K. Cheung, and Guibo Luo, “Uncertainty-Aware Hypergraph Consistency Learning for Semi-supervised Medical Image Segmentation,” in Medical Image Computing and Computer Assisted Intervention (MICCAI-26), 2026.
  4. Zijie Chen, Kejing Yin*, Wenfang Yao, William K. Cheung, and Jing Qin, “MASAM: Multimodal Adaptive Sharpness-Aware Minimization for Heterogeneous Data Fusion,” in The Fourteenth International Conference on Learning Representations (ICLR-26), 2026. [PDF, Code]
  5. Sixing Yan, Ziao Wang, Kejing Yin*, William K. Cheung, Ka Chun Cheung, and Simon See, “Learning Self-Critiquing Mechanisms for Region-Guided Chest X-Ray Report Generation,” in The Fourteenth International Conference on Learning Representations (ICLR-26), 2026. [PDF]

2025

  1. Chen Liu, Wenfang Yao, Kejing Yin*, William K. Cheung, and Jing Qin, “Multimodal Disease Progression Modeling via Spatiotemporal Disentanglement and Multiscale Alignment,” in Advances in Neural Information Processing Systems (NeurIPS-25), 2025. (spotlight) [PDF, Code, Slides, Poster]
    Spotlight: top 3.55% among 21,575 submissions
  2. Ziao Wang, Sixing Yan, Kejing Yin*, Xiaofeng Zhang, and William K. Cheung, “CURV: Coherent Uncertainty-Aware Reasoning in Vision-Language Models for X-Ray Report Generation,” in Advances in Neural Information Processing Systems (NeurIPS-25), 2025. [PDF]

2024

  1. Wenfang Yao, Chen Liu, Kejing Yin*, William K. Cheung, and Jing Qin, “Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation,” in Advances in Neural Information Processing Systems (NeurIPS-24), 2024. [PDF, Code]
  2. Feilong Wu, Kejing Yin, and William K. Cheung, “An End-to-end Learning Approach for Counterfactual Generation and Individual Treatment Effect Estimation,” in 2024 IEEE Conference on Artificial Intelligence (CAI), 2024, pp. 176–182. [PDF, Publisher]
  3. Wenfang Yao, Kejing Yin, William K. Cheung, Jia Liu, and Jing Qin, “DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2024, pp. 16416–16424. [PDF, Publisher, Code]
    Acceptance ratio: 2342⁄9862 = 23.75%
  4. Zhenmiao Zhang, Jin Xiao, Hongbo Wang, Chao Yang, Yufen Huang, Zhen Yue, Yang Chen, Lijuan Han, Kejing Yin, Aiping Lyu, Xiaodong Fang, and Lu Zhang, “Exploring high-quality microbial genomes by assembling short-reads with long-range connectivity,” Nature Communications, vol. 15, 2024. [PDF, Publisher]
  5. Chao Yang, Zhenmiao Zhang, Yufen Huang, Xuefeng Xie, Herui Liao, Jin Xiao, Werner Pieter Veldsman, Kejing Yin, Xiaodong Fang, and Lu Zhang, “LRTK: a platform agnostic toolkit for linked-read analysis of both human genome and metagenome,” GigaScience, vol. 13, 2024.
  6. Jianxuan Huang, Baoyao Yang, Kejing Yin, and Jingwen Xu, “DNA-T: Deformable Neighborhood Attention Transformer for Irregular Medical Time Series,” IEEE Journal of Biomedical and Health Informatics, 2024. [Publisher, Code]
  7. Kejing Yin*, Dong Qian, and 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, 2024. [PDF, Publisher]

2023

  1. Chin Wang Cheong, Kejing Yin, William K Cheung, Benjamin CM Fung, and Jonathan Poon, “Adaptive Integration of Categorical and Multi-relational Ontologies with EHR Data for Medical Concept Embedding,” ACM Transactions on Intelligent Systems and Technology, vol. 14, no. 6, pp. 1–20, 2023. [Publisher]

2022

  1. Kejing Yin*, William K Cheung, Benjamin CM Fung, and Jonathan Poon, “Learning Inter-Modal Correspondence and Phenotypes From Multi-Modal Electronic Health Records,” IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 09, pp. 4328–4341, 2022. [PDF, Publisher, Code]

2021

  1. Ardavan Afshar, Kejing Yin, Sherry Yan, Cheng Qian, Joyce Ho, Haesun Park, and Jimeng Sun, “SWIFT: Scalable Wasserstein factorization for sparse nonnegative tensors,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, pp. 6548–6556. [PDF, Publisher, Code]
    Acceptance ratio: 1692⁄7911 = 21.4%
  2. Kejing Yin, William K Cheung, Benjamin CM Fung, and Jonathan Poon, “TedPar: Temporally dependent PARAFAC2 factorization for phenotype-based disease progression modeling,” in Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), 2021, pp. 594–602. [PDF, Publisher]
    Acceptance ratio: 85⁄400 = 21.25%

2020

  1. Kejing Yin, Ardavan Afshar, Joyce C Ho, William K Cheung, Chao Zhang, and Jimeng Sun, “LogPar: Logistic PARAFAC2 factorization for temporal binary data with missing values,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020, pp. 1625–1635. [PDF, Publisher, Code]
    Research Track; acceptance ratio: 216⁄1279 = 16.9%
  2. Kejing Yin, Liaoliao Feng, and William K Cheung, “Context-aware time series imputation for multi-analyte clinical data,” Journal of Healthcare Informatics Research, vol. 4, pp. 411–426, 2020. [PDF, Publisher, Code, Dataset]
    This is an extension of our previous two-page abstract appeared in ICHI-19.

2019

  1. Kejing Yin, Dong Qian, William K Cheung, Benjamin CM Fung, and Jonathan Poon, “Learning phenotypes and dynamic patient representations via RNN regularized collective non-negative tensor factorization,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, pp. 1246–1253. [PDF, Publisher, Code]
    Acceptance ratio: 1150⁄7095 = 16.2%
  2. Lihong Song, Chin Wang Cheong, Kejing Yin, William K Cheung, Benjamin C M Fung, and Jonathan Poon, “Medical Concept Embedding with Multiple Ontological Representations,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence, 2019, pp. 4613–4619. [PDF, Publisher, Code]
    Acceptance ratio: 850⁄4752 = 17.9%
  3. Kejing Yin and William K Cheung, “Context-aware imputation for clinical time series,” in 2019 IEEE International Conference on Healthcare Informatics (ICHI), 2019, pp. 1–3. [PDF, Publisher, Code]
    Challenge track; two-page abstract

2018

  1. Kejing Yin, William K Cheung, Yang Liu, Benjamin C M Fung, and Jonathan Poon, “Joint Learning of Phenotypes and Diagnosis-Medication Correspondence via Hidden Interaction Tensor Factorization,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence, 2018, pp. 3627–3633. [PDF, Publisher, Code, Dataset]
    Acceptance ratio: 710⁄3470 = 20%
  2. Shunchun Yao, Lifeng Zhang, Kejing Yin, Kaijie Bai, Jialong Xu, Zhimin Lu, and Jidong Lu, “Identifying laser-induced plasma emission spectra of particles in a gas–solid flow based on the standard deviation of intensity across an emission line,” Journal of Analytical Atomic Spectrometry, vol. 33, no. 10, pp. 1676–1682, 2018. [Publisher]
    This is an extension of my undergraduate final-semester project.

2015

  1. Shunchun Yao, Yueliang Shen, Kejing Yin, Gang Pan, and Jidong Lu, “Rapidly measuring unburned carbon in fly ash using molecular CN by laser-induced breakdown spectroscopy,” Energy & Fuels, vol. 29, no. 2, pp. 1257–1263, 2015. [Publisher]
    This is a part of my undergraduate research.