April Morton

April Morton
Research Associate

Phone: (865) 576-9318
Email: mortonam@ornl.gov

Education

MS, Applied Mathematics, California State Polytechnic University, Pomona (2013).
BS, Pure Mathematics, California State Polytechnic University, Pomona (2010).

Professional Experience

2014 – Present, Post-Master’s Research Associate, Oak Ridge National Laboratory, Oak Ridge, TN
2013-2014, Post-Master’s Research Associate, University of Geneva, Geneva, Switzerland
2012 – 2013, Post-Bachelor’s Research Associate, Oak Ridge National Laboratory, Oak Ridge, TN
2012 – 2012, Higher Education Research Experiences Fellow, Oak Ridge National Laboratory, Oak Ridge, TN
2011 – 2011, Research Fellow, Jet Propulsion Laboratory, Pasadena, CA
2010 – 2012, Data Analyst, Southern California Edison, Chino, CA

Research Experience and Interests

Ms. Morton initially joined the Geographic Information Science and Technology (GIST) group at ORNL in June 2012 as a Higher Education Research Experiences (HERE) Fellow, became a Post-Bachelor’s Research Associate in September 2012, and joined the group as a Post-Master’s Research Associate in September 2014. She is currently working on projects involving statistical inference and machine learning as they relate to the urban dynamics and population modeling fields.


Prior to joining the GIST group in 2014, Ms. Morton spent a year in the Viper group in the Computer Vision and Multimedia Laboratory (CVML) at the University of Geneva, working on research issues related to the use and development of machine learning, information retrieval, and pattern recognition methods for the digital analysis of Mayan hieroglyphic images. Before her work with CVML and during her Master’s Degree she developed population models in the GIST group at ORNL, researched and implemented statistical validation techniques for structural vibration models at NASA’s Jet Propulsion Laboratory, and worked as a Data Analyst for Southern California Edison’s Safety and Environmental Programs and Services group.


A common theme within Ms. Morton’s work has been the application and development of machine learning, data mining, computer vision, information retrieval, and other statistical and mathematical techniques for solving problems in a wide variety of domains. Currently, she is interested in the applications of machine learning and statistical modeling for tackling issues related to high resolution spatiotemporal population modeling and other critical areas of study in the urban dynamics field.

Detailed Biosketch