** Please note that my website has moved. For the most up-to-date information, please visit: https://sites.google.com/udel.edu/klee

Hello! I am Kyungmin Lee, a Ph.D. candidate in Energy and Environmental Policy at the Joseph R. Biden, Jr. School of Public Policy and Administration, University of Delaware. My research interest lies broadly in the area of data-driven policy combining data science, machine learning, and computer vision. I am researching under guidance of Dr. Gregory Dobler in the Urban Observatory lab.

Bio

Kyungmin Lee is a Ph.D. candidate in Energy and Environmental Policy at the University of Delaware. My research interests lie at the intersection of energy, climate change, urban development, and social sustainability. With a broad research interest covering areas of environmental planning in international and regional contexts, I am interested in understanding dynamic interactions between humans and the environment. My current research focuses on the impact of human behaviors and the built environment on energy use and heat in cities. With a specific interest in quantitative and spatial analysis methods, I have also developed an interest in big data analysis for a public policy using machine learning, image processing, and computer vision techniques. Prior to joining the Ph.D. program, I worked in the field of international environmental development and cooperation at the government research institute, governmental agency, and United Nations. I received a Bachelor of Arts in Economics from Sungshin Women’s University and a Master of City Planning in Environmental Studies from Seoul National University in South Korea.

Research Area

Policy Area

• Energy and Electricity Policy
• Global Climate Policy
• Community Development, and Urban Policy
• Evidence-Based/Data-Driven Policy

Methods and Tools of Analysis

• Data Science for Social Good
• Machine Learning and Public Policy
• Computer Vision and Deep Learning for Government
• Computational Public Policy
• Ethics in Data Science and AI
• Remote Sensing
• Image Processing

Dissertation Description

My research project uses the Urban Observatory methodology for studying dynamics in complex urban systems through time-dependent proximal remote imaging to characterize urban energy metabolism related to energy-end user behavior, urban heat island effect, and data privacy via infrared imaging. This work applies machine learning and computer vision techniques for image processing and suggests evidence-driven policy design in the field of energy and environmental policy research.