Dr Rui Zhu

Senior Lecturer in Statistics

Bayes Business School, Faculty of Actuarial Science and Insurance

Contact

Visit Rui Zhu

Room BR5035, Bunhill Row

Postal address

Bayes Business School
106 Bunhill Row
London
EC1Y 8TZ
United Kingdom

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, Aug 2021 – present
  • Lecturer in statistics, City, University of London, Sep 2018 – Jul 2021
  • Honorary lecturer, University College London, Sep 2018 – present
  • Lecturer in statistics, University of Kent, Sep 2017 – Aug 2018

Languages

Chinese (Mandarin) and English.

Expertise

Primary topics

  • Statistics

Publications

Conference papers and proceedings (2)

  1. 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
  2. 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

Journal articles (24)

  1. 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]

  2. 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]

  3. 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]

  4. 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]

  5. 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]

  6. 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]

  7. 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]

  8. 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]

  9. 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.
  10. 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.
  11. Zhu, R. and Wüthrich, M.V. (2020). Clustering driving styles via image processing. Annals of Actuarial Science pp. 1–15. doi:10.1017/s1748499520000317.

    [publisher’s website]

  12. 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]

  13. 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]

  14. 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.
  15. 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.
  16. 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.
  17. 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]

  18. 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]

  19. 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.
  20. 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]

  21. 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.
  22. 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]

  23. 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.
  24. 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 paper

  1. Tsanakas, A. and Zhu, R. (2021). Copula model selection using image recognition.