Dr Laureta Hajderanj

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.