Knowledge Discovery and Data Mining (KDD) Lab

Knowledge Discovery and Data Mining (KDD) is the nontrivial process of extracting implicit, novel, and useful information from large volume of data. It has emerged as a unique combination of several fields of science and technology including statistics, database systems, computer programming, machine learning, and artificial intelligence. KDD spans a wide range of applications in Engineering (intrusion detection and network security, flow classification, Web mining), business (fraud detection, decision support systems, risk analysis, forecasting market trend), medicine and population health (study of drug implications, disease outbreak), bioinformatics (protein interactions, gene sequence analysis), and environmental science (flood prediction).

The Knowledge Discovery and Data mining research group compromises of graduate students and researchers from multidisciplinary areas of Systems Science, Engineering, and E-Business. It will further be expanded to include students and researchers from faculties of Mathematics&Statistics and Health Science.

The research projects in the KDD lab focus on both novel techniques, and emerging applications of data mining in Engineering, healthcare, and business. In particular, the focal points of the projects are study and development of (a) advanced algorithms in stream data mining including bio-inspired algorithms; and (b) emerging applications of data mining in the areas of Engineering (network security, intrusion detection), Healthcare (study of health coverage, predicting high-cost patients, and risk of hospitalization), and business (fraud detection, risk analysis, and business analytics).

Graduate students interested in conducting research in these areas are encouraged to send their applications to braahemi@uottawa.ca. Please include a copy of your resume summarizing your related education and work experience, and a statement of your research interests.