Seven N.C. Students Named to Fifth Cohort of Energy Data Analytics Ph.D. Student Fellows

This summer, a group of doctoral students will participate in the fifth cohort of the Energy Data Analytics Ph.D. Student Fellows Program at Duke University’s Nicholas Institute for Energy, Environment & Sustainability. The seven students—from Duke University, North Carolina A&T State University, North Carolina State University, and the University of North Carolina at Chapel Hill—will conduct research on topics including renewable energy, energy forecasting, efficient lightning, coal pollution, energy equity and extreme weather impacts on energy systems.

The program is designed to prepare the next generation of scholars to deftly wield data in pursuit of accessible, affordable, reliable and clean energy systems. Over the summer, fellows will work with faculty from multiple disciplines on their individual research projects. They will also participate in workshops focused on energy systems and data science techniques and develop skills for communicating with interdisciplinary audiences.

The 2023 cohort of Energy Data Analytics Ph.D. fellows. Back row (left to right): Zehao Jin, Simachew Ashebir, Cameron Lisy, Elizabeth Brown. Front row (left to right): Katherine Burley, Amanda Gregg, Ying Yu.

Fellows receive funding support for the duration of the program, as well as a discretionary fund for further research tools and professional development. The fellows will participate in the Energy Data Analytics Symposium (Oct. 26-27, 2023), when expert researchers and practitioners will join early-career scholars and professionals at Duke to discuss how to accelerate sustainable energy systems in the era of artificial intelligence.

“The fellows program cultivates a community of emerging scholars at our partner North Carolina universities who are interested in questions at the intersection of energy and data science,” said Brian Murray, interim director of the Nicholas Institute. “Interdisciplinary exchanges with faculty and one another help the fellows fine-tune their efforts to illuminate energy systems’ challenges and identify high-impact solutions.”  

The first five cohorts of fellows in the program have included Ph.D. students in civil and environmental engineering; computer science; data science and analytics; earth and ocean sciences; economics; electrical and computer engineering; environmental policy; environmental science and engineering; geospatial analysis; city and regional planning; mechanical engineering and materials science; operations research; parks, recreation, and tourism management; and public policy.

The fellows program is organized by Duke’s Energy Data Analytics Lab, a collaboration among the Nicholas Institute, the Rhodes Information Initiative at Duke, and the Social Science Research Institute. The program is open to students at Duke University, North Carolina A&T State University, North Carolina State University, the University of North Carolina at Chapel Hill, the University of North Carolina at Charlotte and the University of North Carolina at Greensboro.

The Energy Data Analytics Ph.D. Student Fellows Program is funded by a grant from the Alfred P. Sloan Foundation. (Note: Conclusions reached or positions taken by researchers or other grantees represent the views of the grantees themselves and not those of the Alfred P. Sloan Foundation or its trustees, officers or staff.) 

Meet the 2023 Fellows

• Simachew Ashebir is a Ph.D. student in data science and analytics at North Carolina A&T State University’s College of Science and Technology. His research interest is time-series data forecasting.
  Project: Time-Series Energy Demand Forecasting Using Hybrid and Deep Learning Methods
   
• Elizabeth Brown is a Ph.D. student in public policy at the University of North Carolina at Chapel Hill’s College of Arts and Sciences. Her research focuses on designing policy interventions to reduce energy poverty with low-carbon and renewable technologies.
  Project: Automating Mini-Grid Distribution Maps
   
• Katherine Burley is a Ph.D. student in public policy at the University of North Carolina at Chapel Hill’s College of Arts and Sciences. Her research interests include energy and climate policy, in particular, subnational policy responses to climate change and the renewable energy transition.
  Project: Restoration Equity: Assessing Inequalities in Electricity Restoration in the Southeast Through Night-Time Lights
   
• Amanda Gregg is a Ph.D. student in materials science and engineering at Duke University’s Pratt School of Engineering. She is working on developing energy-efficient artificial lighting technologies using metamaterials.
  Project: Metamaterials for Energy-Efficient Indoor Lighting
   
• Zehao Jin is a Ph.D. candidate in environmental engineering at Duke University’s Pratt School of Engineering. His major research interest is using machine learning and spatial data analysis techniques to develop an estimation system for the reuse potential of coal combustion byproducts.
  Project: Data-Informed Resources for Coal Fly Ash and Potential Spatial Distribution of Pollutants from Coal Ash Disposal Sites
   
• Cameron Lisy is a Ph.D. student in operations research at North Carolina State University’s Colleges of Engineering and Science. His research objective is to provide decision support tools for the complete decarbonization of the US energy system and to help policy makers better understand the hidden risks to the electric power grid not considered with standard tools and practices.
  Project: Nationwide Impacts of Extreme Weather on the U.S. Natural Gas Production and Transportation Market and Associated Spillover Effects to Major Electric Power Grid Interconnections
   
• Ying Yu is a Ph.D. student in environmental science and engineering at the University of North Carolina at Chapel Hill’s Gillings School of Public Health. She is interested in using microeconomic methods to answer research questions related to climate and energy, with a particular focus on equity discussions.
  Project: Revisiting Energy Equity from a Climate Perspective using Space-Time Models