Energy Data Analytics Ph.D. Student Fellows Program announces 2021 cohort with students from Duke University, NC State University, and UNC Charlotte

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Wednesday, Apr 21, 2021 - 1:54 pm

The Duke University Energy Initiative has unveiled its newest cohort of Energy Data Analytics Ph.D. Student Fellows, which, for the first time, includes doctoral students beyond Duke as part of the program’s expansion. This year’s seven Ph.D. Fellows are from Duke University, North Carolina State University, and the University of North Carolina at Charlotte.

This one-of-a-kind fellows program is designed to produce scholars with expertise in both data science and energy application domains and enables collaboration across universities in the region.

“The Energy Data Analytics Ph.D. Student Fellows Program continues to break the mold of a typical doctoral training venture,” says Brian Murray, director of the Duke University Energy Initiative. “It is interdisciplinary in nature and it opens connections to other universities, so we’re able to innovate in key areas that broaden our ability to build a more accessible, affordable, reliable, and clean energy system.”

Each fellow will conduct a related research project applying data science techniques to energy applications this summer, working with faculty advisors from multiple disciplines. Fellows will participate in regular mentorship workshops as they develop their research. Learn more about the fellows’ backgrounds and their 2021-2022 research projects. The 2021 cohort includes:

  • Yang Deng
    Yang is a Ph.D. student in electrical and computer engineering at Duke University. He is interested in accelerating inverse design of revolutionary energy materials based on all-dielectric metasurfaces through deep learning. 
  • Qian Luo
    Qian is a Ph.D. candidate in environmental engineering at North Carolina State University. Her research focuses on developing strategies to mitigate negative human health impacts associated with power sector emissions by studying the interactions between power sector emissions, air quality, and human health. 
  • Suhas Raju
    Suhas is a Ph.D. student in electrical engineering at the University of North Carolina at Charlotte. His research focuses on energy efficiency through the use of artificial intelligence techniques, seeking to identify solutions to reduce greenhouse gas emissions in the commercial building sector.
  • Josh Randall
    Josh is a Ph.D. candidate in parks, recreation, and tourism management at North Carolina State University. He is a geographer interested in using spatial analysis to realize the equitable access of resources in society, particularly through informing policy and community action.
  • Simiao (Ben) Ren
    Ben is a Ph.D. student in electrical and computer engineering at Duke University. Working in Duke’s Applied Machine Learning Lab, he is interested in applying advanced deep learning techniques to develop novel algorithms that can extract energy systems information from unmanned aerial vehicle (UAV) imagery
  • Celine Robinson
    Celine is a Ph.D. candidate in civil and environmental engineering at Duke University. She is interested in applying deep learning techniques and Bayesian statistics to model and assess natural-hazard-triggered technological (natech) risk in complex and interconnected systems.
  • Zhenxuan Wang
    Zhenxuan is a Ph.D. student in the University Program of Environmental Policy at Duke University with an economics concentration. Sitting at the intersection of environmental economics, climate change, and industrial organization, his research employs innovative data and methods to explore human and firm behavioral responses to environmental changes, and evaluate the efficiency and distributional effect of environmental policies.

In addition to a stipend and partial tuition remission during the summer, fellows will receive up to $1,500 in funding for research and professional development.

Students will receive research mentorship, training on a wide array of energy and data science topics, and research communication advice to broaden the impact of their work. Research outputs can include publications, datasets or code repositories, and video presentations. The first two cohorts of fellows have collectively produced 19 journal or conference papers/presentations, 11 video presentations, and 6 code repositories and datasets to-date.

The program is organized by Duke’s Energy Data Analytics Lab, a collaboration among three of the university’s signature interdisciplinary units: Duke University Energy Initiative (which houses it), Rhodes Information Initiative, and Social Science Research Institute (SSRI).

Duke’s 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.)