EDAL fellows.

The Duke University Energy Initiative has selected its second cohort of Energy Data Analytics Ph.D. Student Fellows, which will include doctoral students in civil and environmental engineering, computer science, earth and ocean sciences, and electrical and computer engineering.

Launched in 2018, the one-of-a-kind fellows program is designed to produce scholars with expertise in both data science and energy application domains.

“Recent developments in machine learning and data science techniques have paved the way for better decisions about how we generate, transmit, and consume energy,” explains Brian Murray, director of the Duke University Energy Initiative. “The doctoral fellows program is designed to push past traditional disciplinary boundaries to prepare next-generation scholars to pursue the accessible, affordable, reliable, and clean energy systems our world needs.”

The second cohort of fellows include four doctoral students:

Alina Barnett

Alina is a second-year computer science Ph.D. student working in the Prediction Analysis Lab. Her project focuses on identifying buildings with poor insulation to better inform civic planning and policy.

Bohao Huang

Bohao Huang is a second-year Ph.D. student in electrical and computer engineering at the Pratt School of Engineering. Working in the Applied Machine Learning Lab, he is interested in leveraging advances in deep learning to develop algorithms that can automatically extract energy systems information from aerial imagery.

Jun Shepard

Jun is a Ph.D. student in earth and ocean sciences at the Nicholas School of the Environment. By studying energy systems in the context of trade, Jun hopes to better understand international energy security.

Tongshu Zheng

Tongshu Zheng is a Ph.D. student in civil and environmental engineering at Duke’s Pratt School of Engineering. Tongshu’s project considers leveraging data-driven techniques to develop a satellite-based remote sensing algorithm to accurately assess the loss in solar energy production due to particulate matter air pollution.

Each fellow will conduct a related research project for nine months, working with faculty from multiple disciplines. Learn more about the fellows’ backgrounds and their 2019-2020 research projects. 

In addition to funding equivalent to one-half of a full fellowship for an academic year, fellows will receive conference travel support and data acquisition support up to $2,000, as well as priority access to virtual machines, storage, and other computational resources. Research conducted by the first two cohorts of fellows will be highlighted at a symposium at Duke University in spring 2020.

Students in the first cohort of fellows have affirmed the value of the program’s multidisciplinary approach, reporting that it has strengthened dissertation chapters, encouraged them to present their work publicly, provided computational resources, and driven their engagement with real-world energy problems.

The program is organized by the 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 PhD 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.)

Register now to check out the 2018-2019 fellows’ final presentations and meet the new 2019-2020 fellows on May 17, 2019. Free lunch provided!

Originally posted on the Energy Initiative website