Dr Rui Zhu

Senior Lecturer in Statistics

Bayes Business School , Faculty of Actuarial Science and Insurance

Contact

About

Overview

Rui joined Bayes in September 2018. Her research interests are in statistical learning and its interdisciplinary applications, including classification and dimension reduction for high-dimensional data, distance metric learning, spectral data analysis, image quality assessment and hyperspectral image analysis.

Qualifications

  • PhD in Statistics, University College London, United Kingdom
  • MSc in Statistics, University College London, United Kingdom
  • BEng, Xiamen University, China

Employment

  • Senior lecturer in statistics, City, University of London, UK, August 2021 - present
  • Lecturer in statistics, City, University of London, United Kingdom, September 2018 - July 2021
  • Honorary lecturer, University College London, UK, September 2018 - present
  • Lecturer in statistics, University of Kent, United Kingdom, September 2017 - August 2018

Languages

Chinese (Mandarin) and English

Expertise

Primary topics

  • Statistics

Publications

Conference papers and proceedings (8)

  1. Bai, X., Yang, Y., Yang, W., Zhu, R. and Xue, J.-.H. (2025). Identity-Preserving Diffusion for Face Restoration. ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 6-11 April.doi:10.1109/icassp49660.2025.10888736
  2. Dong, F., Chen, M., Zhou, J., Shi, Y., Chen, Y., Dong, M.... Zhang, C. (2024). Once Read is Enough: Domain-specific Pretraining-free Language Models with Cluster-guided Sparse Experts for Long-tail Domain Knowledge. NeurIPS 2024 10-15 December, Vancouver, Canada.
  3. Ren, K., Guo, Z., Zhang, Z., Zhu, R. and Li, X. (2022). Multi-Branch Network for Few-shot Learning. 2022 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 7-10 November.doi:10.23919/apsipaasc55919.2022.9980160
  4. Bao, Q., Zhu, R., Gang, B., Zhao, P., Yang, W. and Liao, Q. Distilling Resolution-robust Identity Knowledge for Texture-Enhanced Face Hallucination. MM '22: The 30th ACM International Conference on Multimedia.doi:10.1145/3503161.3548437
  5. Guo, J., Bao, Q., Zhu, R., Yang, W. and Liao, Q. (2022). Quality-Oriented Feature Regression for Robust Image Similarity Metric. 2022 IEEE International Conference on Multimedia and Expo (ICME) 18-22 July.doi:10.1109/icme52920.2022.9859721
  6. Lv, Y., Yang, W., Zuo, W., Liao, Q. and Zhu, R. (2022). Sain: Similarity-Aware Video Frame Interpolation. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 23-27 May.doi:10.1109/icassp43922.2022.9747211
  7. Dong, M., Yang, X., Zhu, R., Wang, Y. and Xue, J.H. Generalization bound of gradient descent for non-convex metric learning. .
  8. Bao, Q., Hui, Z., Zhu, R., Ren, P., Xie, X. and Yang, W. Improving Diffusion-Based Image Restoration with Error Contraction and Error Correction. .doi:10.1609/aaai.v38i2.27833

Journal articles (43)

  1. Dong, J., Zhu, R., Shang, X. and Xue, J.-.H. (2026). Federated learning with noisy labels: A comprehensive and concise review of current methodologies and future directions. Neural Networks, 201, pp. 108889-108889. doi:10.1016/j.neunet.2026.108889

    [publisher’s website]

  2. Dong, J., Zhu, R., Shang, X. and Xue, J.-.H. (2026). Dawid-Skene-model-based label-noise mitigation for federated learning. Information Sciences, 745, pp. 123425-123425. doi:10.1016/j.ins.2026.123425

    [publisher’s website]

  3. Zheng, H., Wang, H., Zhu, R. and Xue, J.-.H. (2026). A brief review of deep learning methods in mortality forecasting. Annals of Actuarial Science, 20(1), pp. 150-165. doi:10.1017/s1748499525100110

    [publisher’s website]

  4. Lu, P., Li, X., Zhu, R., Ma, Z., Cao, J. and Xue, J.-.H. (2026). Fine-Tuning via Linked Domains: A Closed-Form Dual Alignment Mechanism for Transferring Vision-Language Models. IEEE Transactions on Circuits and Systems for Video Technology, 36(3), pp. 3613-3623. doi:10.1109/tcsvt.2025.3613794

    [publisher’s website]

  5. Wang, X., Zhu, R. and Xue, J.-.H. (2026). UC-PUAL: A universally consistent classifier of positive-unlabelled data. Pattern Recognition, 169, pp. 111892-111892. doi:10.1016/j.patcog.2025.111892

    [publisher’s website]

  6. Zheng, H., Wang, H., Zhu, R. and Xue, J.-.H. (2025). Fine-grained mortality forecasting with deep learning. Annals of Actuarial Science pp. 1-27. doi:10.1017/s1748499525100171

    [publisher’s website]

  7. Li, X., Wang, L., Zhu, R., Ma, Z., Cao, J. and Xue, J.-.H. (2025). SRML: Structure-relation mutual learning network for few-shot image classification. Pattern Recognition, 168, pp. 111822-111822. doi:10.1016/j.patcog.2025.111822

    [publisher’s website]

  8. Li, X., Ji, L., Zhu, R., Ma, Z. and Xue, J.-.H. (2025). Clarity in chaos: Boosting few-shot classification through information suppression and sparsification. Pattern Recognition, 167, pp. 111726-111726. doi:10.1016/j.patcog.2025.111726

    [publisher’s website]

  9. Wang, C., Yan, S., Chen, Y., Wang, X., Wang, Y., Dong, M.... Shang, L. (2025). Denoising Reuse: Exploiting Inter-Frame Motion Consistency for Efficient Video Generation. IEEE Transactions on Circuits and Systems for Video Technology, 35(9), pp. 8436-8451. doi:10.1109/tcsvt.2025.3548728

    [publisher’s website]

  10. Wang, X., Yang, X., Zhu, R. and Xue, J.-.H. (2025). PUAL: A classifier on trifurcate positive-unlabelled data. Neurocomputing, 637, pp. 130080-130080. doi:10.1016/j.neucom.2025.130080

    [publisher’s website]

  11. De Mori, L., Haberman, S., Millossovich, P. and Zhu, R. (2025). Mortality forecasting via multi-task neural networks. ASTIN Bulletin, 55(2), pp. 313-331. doi:10.1017/asb.2025.10

    [publisher’s website]

  12. Li, X., Lu, P., Zhu, R., Ma, Z., Cao, J. and Xue, J.-.H. (2025). Rise by Lifting Others: Interacting Features to Uplift Few-Shot Fine-Grained Classification. IEEE Transactions on Circuits and Systems for Video Technology, 35(4), pp. 3094-3103. doi:10.1109/tcsvt.2024.3501733

    [publisher’s website]

  13. Wang, X., Zhu, R. and Xue, J.-.H. (2025). GKF-PUAL: A group kernel-free approach to positive-unlabeled learning with variable selection. Information Sciences, 690. doi:10.1016/j.ins.2024.121574

    [publisher’s website]

  14. Asimit, V., Wang, R., Zhou, F. and Zhu, R. (2025). Efficient Positive Semidefinite Matrix Approximation by Iterative Optimisations and Gradient Descent Method. Risks, 13(2), pp. 28-28. doi:10.3390/risks13020028

    [publisher’s website]

  15. Li, X., Wang, X., Zhu, R., Ma, Z., Cao, J. and Xue, J.-.H. (2025). Selectively Augmented Attention Network for Few-Shot Image Classification. IEEE Transactions on Circuits and Systems for Video Technology, 35(2), pp. 1180-1192. doi:10.1109/tcsvt.2024.3480279

    [publisher’s website]

  16. Zhang, Z., Chang, D., Zhu, R., Li, X., Ma, Z. and Xue, J.-.H. (2025). Query-Aware Cross-Mixup and Cross-Reconstruction for Few-Shot Fine-Grained Image Classification. IEEE Transactions on Circuits and Systems for Video Technology, 35(2), pp. 1276-1286. doi:10.1109/tcsvt.2024.3484530

    [publisher’s website]

  17. Li, X., Guo, Z., Zhu, R., Ma, Z., Guo, J. and Xue, J.-.H. (2024). A simple scheme to amplify inter-class discrepancy for improving few-shot fine-grained image classification. Pattern Recognition, 156, pp. 110736-110736. doi:10.1016/j.patcog.2024.110736

    [publisher’s website]

  18. Shi, Y., Cao, H., Xia, B., Zhu, R., Liao, Q. and Yang, W. (2024). DSR-Diff: Depth map super-resolution with diffusion model. Pattern Recognition Letters, 184, pp. 225-231. doi:10.1016/j.patrec.2024.06.025

    [publisher’s website]

  19. Xu, Z., Xu, H., Lu, Z., Zhao, Y., Zhu, R., Wang, Y.... Shang, L. (2024). Can Large Language Models Be Good Companions? Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 8(2), pp. 1-41. doi:10.1145/3659600

    [publisher’s website]

  20. De Mori, L., Millossovich, P., Zhu, R. and Haberman, S. (2024). Two-Population Mortality Forecasting: An Approach Based on Model Averaging. Risks, 12(4), pp. 60-60. doi:10.3390/risks12040060

    [publisher’s website]

  21. Li, X., Song, Q., Wu, J., Zhu, R., Ma, Z. and Xue, J.-.H. (2023). Locally-Enriched Cross-Reconstruction for Few-Shot Fine-Grained Image Classification. IEEE Transactions on Circuits and Systems for Video Technology, 33(12), pp. 7530-7540. doi:10.1109/tcsvt.2023.3275382

    [publisher’s website]

  22. Qi, X., Lu, Q., Pan, W., Zhao, Y., Zhu, R., Dong, M.... Shang, L. (2023). CASES. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 7(3), pp. 1-31. doi:10.1145/3610910

    [publisher’s website]

  23. Zhu, R., Zhou, F., Yang, W. and Xue, J.-.H. (2023). Statistical hypothesis testing as a novel perspective of pooling for image quality assessment. Signal Processing: Image Communication, 114, pp. 116942-116942. doi:10.1016/j.image.2023.116942

    [publisher’s website]

  24. Li, X., Li, Y., Zheng, Y., Zhu, R., Ma, Z., Xue, J.-.H.... Cao, J. (2023). ReNAP: Relation network with adaptiveprototypical learning for few-shot classification. Neurocomputing, 520, pp. 356-364. doi:10.1016/j.neucom.2022.11.082

    [publisher’s website]

  25. Tsanakas, A. and Zhu, R. (2022). SELECTING BIVARIATE COPULA MODELS USING IMAGE RECOGNITION. ASTIN Bulletin, 52(3), pp. 707-734. doi:10.1017/asb.2022.12

    [publisher’s website]

  26. Asimit, A.V., Kyriakou, I., Santoni, S., Scognamiglio, S. and Zhu, R. (2022). Robust Classification via Support Vector Machines. Risks, 10(8), pp. 154-154. doi:10.3390/risks10080154

    [publisher’s website]

  27. Wang, Z., Liu, Y., Zhu, R., Yang, W. and Liao, Q. (2022). Lightweight Single Image Super-Resolution With Similar Feature Fusion Block. IEEE Access, 10, pp. 30974-30981. doi:10.1109/access.2022.3158936

    [publisher’s website]

  28. Sogi, N., Zhu, R., Xue, J.-.H. and Fukui, K. (2021). Constrained mutual convex cone method for image set based recognition. Pattern Recognition pp. 108190-108190. doi:10.1016/j.patcog.2021.108190

    [publisher’s website]

  29. Zhu, R. and Wüthrich, M.V. (2021). Clustering driving styles via image processing. Annals of Actuarial Science, 15(2), pp. 276-290. doi:10.1017/s1748499520000317

    [publisher’s website]

  30. Lu, X., Qiao, Y., Zhu, R., Wang, G., Ma, Z. and Xue, J.H. (2021). Generalisations of stochastic supervision models. Pattern Recognition, 109, pp. 107575.-107575.. doi:10.1016/j.patcog.2020.107575

    [publisher’s website]

  31. Yang, W., Zhou, F., Zhu, R., Fukui, K., Wang, G. and Xue, J.-.H. (2020). Deep learning for image super-resolution. Neurocomputing, 398, pp. 291-292. doi:10.1016/j.neucom.2019.09.091

    [publisher’s website]

  32. Zhou, F., Yang, W., Gao, X., Liu, H., Zhu, R. and Xue, J.-.H. (2020). Special Issue on Advances in Statistical Methods-based Visual Quality Assessment. Signal Processing: Image Communication, 83, pp. 115695-115695. doi:10.1016/j.image.2019.115695

    [publisher’s website]

  33. Zhu, R., Guo, Y. and Xue, J.-.H. (2020). Adjusting the imbalance ratio by the dimensionality of imbalanced data. Pattern Recognition Letters. doi:10.1016/j.patrec.2020.03.004

    [publisher’s website]

  34. Zhu, R., Wang, Z., Sogi, N., Fukui, K. and Xue, J.-.H. (2019). A Novel Separating Hyperplane Classification Framework to Unify Nearest-class-model Methods for High-dimensional Data. IEEE Transactions on Neural Networks and Learning Systems. doi:10.1109/TNNLS.2019.2946967

    [publisher’s website]

  35. Zhu, R., Dong, M. and Xue, J.-.H. (2018). Learning distance to subspace for the nearest subspace methods in high-dimensional data classification. Information Sciences, 481, pp. 69-80. doi:10.1016/j.ins.2018.12.061

    [publisher’s website]

  36. Zhu, R., Wang, Z., Ma, Z., Wang, G. and Xue, J.-.H. (2018). LRID: A new metric of multi-class imbalance degree based on likelihood-ratio test. Pattern Recognition Letters, 116, pp. 36-42. doi:10.1016/j.patrec.2018.09.012

    [publisher’s website]

  37. Zhu, R., Zhou, F., Yang, W. and Xue, J.-.H. (2018). On Hypothesis Testing for Comparing Image Quality Assessment Metrics [Tips & Tricks]. IEEE Signal Processing Magazine, 35(4), pp. 133-136. doi:10.1109/msp.2018.2829209

    [publisher’s website]

  38. Wang, Z., Zhu, R., Fukui, K. and Xue, J.-.H. (2018). Cone-based joint sparse modelling for hyperspectral image classification. Signal Processing, 144, pp. 417-429. doi:10.1016/j.sigpro.2017.11.001

    [publisher’s website]

  39. Zhu, R., Zhou, F. and Xue, J.-.H. (2018). MvSSIM: A quality assessment index for hyperspectral images. Neurocomputing, 272, pp. 250-257. doi:10.1016/j.neucom.2017.06.073

    [publisher’s website]

  40. Wang, Z., Zhu, R., Fukui, K. and Xue, J.-.H. (2017). Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case Studies for Hyperspectral Target Detection. IEEE Transactions on Image Processing, 26(11), pp. 5447-5461. doi:10.1109/TIP.2017.2740621

    [publisher’s website]

  41. Zhu, R. and Xue, J.-.H. (2017). On the orthogonal distance to class subspaces for high-dimensional data classification. Information Sciences, 417, pp. 262-273. doi:10.1016/j.ins.2017.07.019

    [publisher’s website]

  42. Zhu, R., Fukui, K. and Xue, J.-.H. (2017). Building a discriminatively ordered subspace on the generating matrix to classify high-dimensional spectral data. Information Sciences, 382-383, pp. 1-14. doi:10.1016/j.ins.2016.12.001

    [publisher’s website]

  43. Zhu, R., Dong, M. and Xue, J.-.H. (2015). Spectral Nonlocal Restoration of Hyperspectral Images With Low-Rank Property. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), pp. 3062-3067. doi:10.1109/jstars.2014.2370062

    [publisher’s website]

Working papers (2)

  1. Abootalebi, Z., Tsanakas, A. and Zhu, R. (2026). Differential Measurement of Proxy Discrimination.
  2. Tsanakas, A. and Zhu, R. Copula model selection using image recognition.