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Dr Laureta Hajderanj

Postdoctoral Fellow in Business Data Analytics

Laureta Hajderanj

Specialisms

  • Applied Statistics, 
  • Dimensionality Reduction, 
  • Unsupervised Learning, 
  • Machine Learning, 
  • Data Mining

Location

G25, Henley Business School, Whiteknights

Dr. Laureta Hajderanj is a researcher and academic specializing in applied statistics, machine learning, and data visualization. Dr. Hajderanj’s interdisciplinary approach bridges statistical theory, computer science, and practical applications in machine learning—making her a valuable voice in the field of trustworthy AI and data interpretation.

Dr. Laureta Hajderanj is a researcher and academic specializing in applied statistics, machine learning, and data visualization. Her work focuses particularly on unsupervised learning, with an emphasis on improving the trustworthiness and computational efficiency of dimensionality reduction algorithms—a crucial step in visualizing and interpreting high-dimensional data. She completed her PhD from London South Bank University, where her dissertation introduced novel approaches to dimensionality reduction that preserve data structure while minimizing computational costs. Throughout her academic career, Dr. Hajderanj has developed both parametric and non-parametric algorithms aimed at enhancing data visualization fidelity without sacrificing performance. Her work is widely published in peer-reviewed journals such as Information Sciences and IEEE Access, where she has explored topics ranging from same-degree distribution-based methods to supervised manifold learning and its effect on classification and data structure preservation. These contributions not only address theoretical underpinnings but also offer practical advancements for data scientists and researchers dealing with complex, high-dimensional datasets. Dr. Hajderanj’s interdisciplinary approach bridges statistical theory, computer science, and practical applications in machine learning—making her a valuable voice in the field of trustworthy AI and data interpretation.

Reference: Hajderanj, L. , Chen, D., Dudley, S., Gilloppe, G. and Sivy, B. (2024) Novel parameter-free and parametric same degree distribution-based dimensionality reduction algorithms for trustworthy data structure preserving. Information Sciences, 661. 120030. ISSN 1872-6291 doi: 10.1016/j.ins.2023.120030
Henley faculty authors:
Dr Laureta Hajderanj
Reference: Fenghour, S., Chen, D., Hajderanj, L. , Weheliye, I. and Xiao, P. (2022) A novel supervised t-SNE based approach of viseme classification for automated lip reading. In: 2021 International Conference on Electrical, Computer and Energy Technologies (ICECET), 9-10 December 2021, Cape Town, South Africa. doi: 10.1109/ICECET52533.2021.9698534
Henley faculty authors:
Dr Laureta Hajderanj
Reference: Hajderanj, L. , Chen, D. and Weheliye, I. (2021) The impact of supervised manifold learning on structure preserving and classification error: a theoretical study. IEEE Access, 9. pp. 43909-43922. ISSN 2169-3536 doi: 10.1109/ACCESS.2021.3066259
Henley faculty authors:
Dr Laureta Hajderanj
Reference: Chen, D., Hajderanj, L. , Mallet, S., Camenen, P., Li, B., Ren, H. and Zhao, E. (2021) Deep learning causal attributions of breast cancer. In: Arai, K. (ed.) Intelligent Computing: Proceedings of the 2021 Computing Conference, Volume 3. Lecture Notes in Computer Science, 285. Springer, Cham, pp. 124-135. ISBN 9783030801281 doi: 10.1007/978-3-030-80129-8_10
Henley faculty authors:
Dr Laureta Hajderanj
Reference: Hajderanj, L. , Chen, D., Grisan, E. and Dudley, S. (2020) Single- and multi-distribution dimensionality reduction approaches for a better data structure capturing. IEEE Access, 8. pp. 207141-207155. ISSN 2169-3536 doi: 10.1109/ACCESS.2020.3038460
Henley faculty authors:
Dr Laureta Hajderanj