Biography:

• Chamila Walgampaya, is a professor in computer engineering at the Faculty of Engineering. In December 2001, Chamila joined the Department of Engineering Mathematics at the University of Peradeniya, marking the commencement of his influential career in academia. He was awarded the Fulbright scholarship in August 2004, leading him to pursue an MS in Computer Engineering at the University of Louisville, Kentucky, U.S.A. He successfully completed his master's degree in May 2006, presenting a thesis titled "Cost-benefit Analysis in Multiple Time Series Prediction," which demonstrated his innovative approach to optimizing sensor selection in distributed energy-producing plant networks. Chamila's academic journey culminated in the attainment of a Ph.D. from the School of Engineering at the University of Louisville in August 2011. His doctoral research, encapsulated in a dissertation titled "Click Fraud: how to spot it? how to stop it?," focused on the development of a pioneering click fraud detection and prevention system for Internet advertising. In addition to his roles as a professor and researcher, Chamila Walgampaya serves as the Director of the computing center at the Faculty of Engineering since March 2021.

Educational Qualifications :

• PhD: University of Louisville, Louisville, KY, USA.
• MSc: University of Louisville, USA
• BSc: University of Peradeniya

Honors and Awards :

• Co-author, Certicate of Merit, International Conference on Soft Computing and Applications 2010.
• E-EXPO Student Research Competition, Graduate overall 1st place and Best in CECS Department, 2010.
• Co-author, Best Paper Award, IADIS International Conference on e commerce, Portugal, 2009.
• Grosscurth Scholarship, Speed School of Engineering, University of Louisville, 2007-2009.
• Fulbright Scholar, 2004-2006.
• Best A/L examination results in 1995, Ceylon Bank Employees Union Scholarship, 1997-2001.
• World Prize, ACS(Australian Computer Society) examination, 1997.

• Click fraud mining, Automatic web robots and agents, Data and evidence fusion, Ensemble methods and machine learning.