Anjana Samarakoon
About Anjana Samarakoon
Anjana Samarakoon is a Postdoctoral Research Associate at Oak Ridge National Laboratory, specializing in materials science and data analysis. With a Ph.D. in Condensed Matter and Materials Physics from the University of Virginia, Samarakoon has contributed to various projects involving advanced materials and machine learning techniques.
Current Position at Oak Ridge National Laboratory
Anjana Samarakoon serves as a Postdoctoral Research Associate at Oak Ridge National Laboratory, a position she has held since 2018. In this role, she focuses on data analysis and modeling for various materials, including Na2Co2, LiGaCr4S8, NaxCoO2, and Mn3Si2Te6. Her work involves planning, executing, and analyzing data from neutron experiments, contributing to advancements in materials science.
Previous Experience at Oak Ridge National Laboratory
Prior to her current role, Anjana Samarakoon worked as a Research Assistant at Oak Ridge National Laboratory from 2015 to 2018. During her tenure at the Shull Wollan Center, she engaged in research that laid the foundation for her subsequent work in data analysis and modeling of materials.
Education and Academic Background
Anjana Samarakoon completed her Doctor of Philosophy (Ph.D.) in Condensed Matter and Materials Physics at the University of Virginia from 2012 to 2017. She also holds a Bachelor of Science (B.Sc.) in Physics from the University of Colombo, which she obtained from 2006 to 2010. Her foundational education was completed at Richmond College in Galle, Sri Lanka, where she studied from 1992 to 2005.
Teaching Experience at University of Virginia
Anjana Samarakoon served as a Teaching Assistant at the University of Virginia from 2012 to 2013. In this role, she supported instructional activities and contributed to the educational development of students in the Charlottesville, Virginia area.
Research Contributions and Projects
Throughout her research career, Anjana Samarakoon has contributed to projects involving frustrated magnetic systems, including Dy2Ti2O7, RuCl3, and Ba3ZnRu2O9. She developed machine learning-assisted modeling approaches for neutron diffuse and inelastic scattering, enabling multi-experiment regression on complex 3D-4D data sets.