ProfessorMohand Tahar Kechadi

My current research interests are primary in: ' Problem of Clustering in Machine Learning and Data Mining, ' Data Mining and Large-Scale Distributed data mining Techniques, ' Digital Forensics and Cybercrime investigations. My current research projects' goals are twofold: Design, develop and implement innovative distributed data mining techniques for real-world data intensive applications, such as healthcare, digital forensics and cybercrime, telecommunication systems, banking and finance. Provide these techniques with services and middleware tools in order to take advantage of the available distributed platforms such as the Cloud Computing. I did my postgraduate research in the area of the architecture of vector machines and their performance. After completing my PhD, I moved into the field of the development of efficient algorithms and tools for real-world applications. My initial research in the area was on the design of efficient parallel algorithms for protein folding applications. These algorithms were implemented on a Maspar MPP with 16K processors. I gained significant experience from these data-intensive real-word applications. The problem then is how to manage and process all these huge volumes of data. Both the analysis of large data-sets and the computing environments created new problems and challenges for efficient execution and optimal system performance. This brought me to look at the challenges of the data mining in the distributed environment. Recently, I am specifically looking at these issues from computer forensics and cybercrime investigation and finance perspectives