April M Morton
April M Morton
Research Scientist, Geographic Data Sciences
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, joined the group as a Post-Master’s Research Associate in September 2014, and became a Research Scientist in July 2016. 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.
Akasiadis, C., Panagidi, K., Panagiotou, N., Sernani, P., Morton, A., Vetsikas, I., Mavrouli, L., Goutsias, K. (2015). Incentives for Rescheduling Residential Electricity Consumption to Promote Renewable Energy Usage. In Proceedings of the SAI Intelligent Systems Conference 2015, London, United Kingdom.
Morton, A., Nagle, N., Piburn, J., Stewart, R., Mcmanamay, R. (2015). A Hybrid Dasymetric and Machine Learning Approach to High-Resolution Residential Electricity Consumption Modeling. In Proceedings of the GeoComputation 2015 Conference, Dallas, Texas.
Piburn, J., Stewart, R., Morton, A. (2015). Attribute Portfolio Distance: A Dynamic Time Warping Based Approach to Comparing and Detecting Common Spatiotemporal Patterns Among Multi-Attribute Data Portfolios. In Proceedings of the GeoComputation 2015 Conference, Dallas, Texas.
Stewart, S., Piburn, J., Weber, E., Urban, M., Morton, A., Thakur, G., Bhaduri, B. (2015). Can Social Media Play a Role in Developing Building Occupancy Curves for Small Area Estimation? In Proceedings of the GeoComputation 2015 Conference, Dallas, Texas.
Morton, A., Marzban, E., Giannoulis, G., Patel, A., Aparasu, R., Kakadiaris, I. (2014). A Comparison of Supervised Machine Learning Techniques for Predicting Short-Term In-Hospital Length of Stay Among Diabetic Patients. In Proceedings of the Workshop on Machine Learning for Predictive Models, Detroit, Michigan.
Osipyan, H., Morton, A., Marchand-Maillet, S. (2014) Fast Interactive Information Retrieval with Sample-Based MDS on GPU Architectures. In Proceedings of the Information Retrieval Facility Conference, Copenhagen, Denmark.
Stewart, R., White, D., Urban, M., Morton, A., Webster, C., Stoyanov, M., Bright, E., & Bhaduri, B. (2013). Uncertainty Quantification Techniques for Population Density Estimates Derived from Sparse Open Source Data. In Proceedings of the SPIE Geospatial InfoFusion III Conference, Baltimore, Maryland.
Stewart, R., Urban, M., Duchscherer, S., Kaufman, J., Morton, A., Thakur, G., Piburn, J., Moehl, J. (2016). A Bayesian Machine Learning Model for Estimating Building Occupancy from Open Source Data. Journal of the International Society for the Prevention and Mitigation of Natural Hazards.
Weng, L., Armsaleg, L., Morton, A., Marchand-Maillet, S. (2014). A Privacy-Preserving Framework for Large-Scale Content-Based Information Retrieval. IEEE Transactions on Information Forensics & Security.