Rama Vasudevan
About Rama Vasudevan
Rama Vasudevan is a Research and Development Staff Scientist at Oak Ridge National Laboratory, specializing in the growth and in-situ scanning tunneling microscopy of complex oxides. He holds a PhD in Materials Science and actively promotes open science while utilizing machine learning techniques to analyze microscopy data.
Work at Oak Ridge National Laboratory
Rama Vasudevan has been employed at Oak Ridge National Laboratory since 2016, serving as a Research and Development Staff Scientist in the Center for Nanophase Materials Sciences. In this role, he focuses on the growth and in-situ scanning tunneling microscopy of complex oxides. His work involves examining the relationship between structural and chemical properties at atomic scales, contributing to advancements in materials science.
Previous Experience at Oak Ridge National Laboratory
Prior to his current position, Rama Vasudevan worked as a Postdoctoral Research Associate at Oak Ridge National Laboratory from 2013 to 2016. During this time, he engaged in research that laid the groundwork for his current projects, further developing his expertise in materials science and nanotechnology.
Education and Expertise
Rama Vasudevan earned his Doctor of Philosophy (PhD) in Materials Science from the University of New South Wales, where he studied from 2010 to 2012. He also holds a Bachelor of Science (B.Sc.) in Nanotechnology from the same institution, completed between 2006 and 2009. His educational background provides a strong foundation for his research in complex oxides and nanomaterials.
Research Specializations
Rama Vasudevan specializes in the growth and in-situ scanning tunneling microscopy of complex oxides. He employs machine learning techniques, such as Gaussian Processes, Decision Trees, and Support Vector Machines, to analyze large microscopy datasets. His work bridges experimental results with theoretical models, translating imaging data into meaningful physical insights.
Advocacy for Open Science
Rama Vasudevan is an active proponent of open science, advocating for transparency and accessibility in research. He leads a reinforcement learning team that addresses material synthesis challenges, emphasizing the importance of sharing knowledge and resources within the scientific community.