Buddhika Samarakoon
About Buddhika Samarakoon
Buddhika Samarakoon is an Applied Research Scientist at Expedia Group with extensive experience in research and data science.
Company
Buddhika Samarakoon is currently employed as an Applied Research Scientist at Expedia Group. He has been part of the team since 2018, based in Greater Seattle Area. His role involves working on advanced research projects and providing data science solutions within the company.
Title and Current Role
As an Applied Research Scientist at Expedia Group, Buddhika Samarakoon focuses on advanced algorithms and data-driven solutions. His contributions include developing and integrating error monitoring libraries for predictive models and aiding campaign managers with valuable insights for metasearch campaigns.
Education and Expertise
Buddhika Samarakoon holds an extensive educational background in engineering and applied sciences. He earned his Doctor of Philosophy (PhD) in Computer Engineering from the University of Miami (2012-2018). His academic journey also includes a Master of Applied Science in Mechatronics from The University of British Columbia (2010-2011) and a Bachelor of Science in Engineering in Electronic and Telecommunication Engineering from the University of Moratuwa (2005-2009).
Past Roles and Experience
Before joining Expedia Group, Buddhika Samarakoon has worked with various prestigious institutions. His prior roles include Research Assistant at the University of Miami (2012-2018), Graduate Research Assistant at The University of British Columbia (2010-2012), Assistant Lecturer at SLIIT (2009-2010), and an Intern at Airport and Aviation Services Ltd in Sri Lanka (2007-2008).
Key Contributions and Projects
Buddhika Samarakoon has made significant contributions in his field, including designing and implementing an iterative clustering algorithm for item grouping in a meta auction marketplace, achieving a 15-20% improvement in RMSE value. He also built a PySpark error monitoring library for Brand Expedia's prediction models, monitoring critical metrics such as RMSE, MAE, and Log Loss on approximately 4.5 million data points daily. This work was integrated with Jenkins CI pipeline, attaining 70% code coverage in test cases.