معرفی
Dr. Nicos Pavlidis is a Senior Lecturer in the Department of Management Science at Lancaster University's Management School. His research focuses on developing novel methods for clustering and classification in high-dimensional and time-varying data environments, with applications in machine learning and statistics. He teaches courses such as MSCI 562 (Intelligent Data Analysis and Visualisation) and MSCI 331 (Data Mining for Direct Marketing and Finance), supported by DataCamp platforms.
His research interests include statistics, data mining, and machine learning, with a particular emphasis on adaptive methods for dynamic data streams. Pavlidis has contributed to projects like STOR-i: DSI (2017–2021), which aimed to improve clustering efficiency in high-dimensional datasets. He is affiliated with the STOR-i Centre for Doctoral Training and the Centre for Marketing Analytics & Forecasting.
His publications span topics such as spectral clustering, metric learning, and ensemble methods. He has advised PhD student Danielle Notice and has reviewed for journals like IEEE Transactions on Evolutionary Computation and Applied Soft Computing. Pavlidis actively participates in conferences and workshops, including the International Symposium on Intelligent Data Analysis and the IEEE Big Data Conference.
