• Qiu, L., et al. (2023) [“Interpretable Deep Learning for the Mystery of Disease Progression”] (Nature Methods Under Revision）

  • Qiu, L., et al. (2023) [“Deep Pathology Genomic Multimodal Survival Prediction”][arXiv] (Nature Machine Intelligence Under Revision)

  • Qiu, L., et al. (2023) [“Predicting Celluar Response from Interpretable Causal Inference”] (ICML 2024 Under Review)

  • Qiu, L., et al. (2023) [“Variational Multi-view Learning”] (Cell Patterns Under Review)

  • Qiu, L., Chinchilli, V. M., and Lin, L. (2022). [“Interpretable Deep Representation Learning from Temporal Multi-view Data”]. Asian Conference on Machine Learning (ACML 2022 Long Oral) [arXiv][Github]

  • Qiu, L., Lin, L., and Chinchilli, V. M. (2022). [“Variational Interpretable Deep Canonical Correlation Analysis”]. International Conference on Learning Representations, Machine Learning for Drug Discovery Workshop (ICLR 2022) [arXiv][Slides]

  • Qiu, L., Llerena, N. L., Sousa, V. S., Lin, L., and Chinchilli, V. M. (2022). [“Probabilistic Model Incorporating Auxiliary Covariates to Control FDR”]. ACM International Conference on Information and Knowledge Management (CIKM 2022) [acm][arxiv][Github]

  • Qiu, L., Sousa, V. S., and Llerena, N. L. (2022). “Visual Tag Emerging Pattern Detection”. US Patent App. 17/120, 933, 2022.

  • Qiu, L., and Chinchilli, V. M. (2022). [“Probabilistic Canonical Correlation Analysis for High-dimensional Sparse Count Data”]. Journal of Statistical Research, 56(1), 75–100.[paper][Github]

  • Qiu, L., Wu, T. T., Dong, H., Wu, L. L., Cao, J. S., Huang, L. (2013). “High-level expression of sporamin in transgenic Chinese cabbage enhances resistance against diamondback moth”. Plant Molecular Biology Reporter, 31, 657-664.

Working papers:

  • Qiu, L., et al. (2024) [“Transformer Multimodal Learning on Pathway-informed Genomics and Pathology”]
  • Qiu, L., et al. (2024) [“Pathological vision-language model”]