Publications

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. 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.
  3. 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]
  4. 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.