Meet Duke University's 2018-2019 cohort of Energy Data Analytics PhD Student Fellows:

Bohao Huang is a Ph.D. student in electrical and computer engineering at Duke's Pratt School of Engineering. He is part of the Applied Machine Learning Lab at Duke. He focuses on the translation of advanced machine learning techniques into practical solutions for challenging real-world problems.

Project summary: Energy security is vital to the prosperity and sustainability of modern societies. Ensuring energy security relies upon effective decision-making and energy systems modeling, a crucial component of which is access to high quality energy systems information. Unfortunately, however such information is often of limited availability, incomplete, or difficult to access because it is proprietary. Aerial imagery (e.g., color satellite imagery) is increasingly cheap and abundant, and may provide a rich source of energy systems information, but extracting useful information from such imagery is costly.  I propose to leverage recent advances in deep learning to develop algorithms that can automatically extract useful energy systems information from large volumes of aerial imagery, potentially yielding a powerful and scalable new source of such information.

You can contact Bohao at bohao.huang@duke.edu

Qingran Li is a Ph.D. student in the University Program in Environmental Policy (economics track) offered jointly by Duke's Nicholas School of the Environment and Sanford School of Public Policy. Her research includes using analytical tools to understand behavioral responses to policies and developing interdisciplinary solutions to energy and environmental issues.

Project summary: "Residential electricity consumption is an important indicator of household characteristics, but it is often held confidential by utilities and seldom reported by publicly available energy surveys. Missing such information significantly constrains our ability to answer important policy questions. My project targets a big question: How can we estimate residential electricity demand more precisely? Using the smart meter data set from an Irish CER trial project and the national time use survey, this project aims at correcting the estimation bias from behavioral and policy-related factors which are often overlooked in the conventional engineering and statistical models. A new algorithm will be developed to identify residential usage patterns with additional information provided by behavioral surveys so that information lost from inadequate load samplings can be compensated."

You can contact Qingran at qingran.li@duke.edu

Edgar Virguez is a student in the doctoral program in environment at Duke's Nicholas School of the Environment. He is interested in contributing to the understanding of market mechanisms that facilitate the integration of variable energy resources. He holds a BS in environmental engineering, a BS in chemical engineering and a MS in environmental engineering. During the last decade, he has worked with several institutions (e.g., Universidad de los Andes, World Bank, Inter-American Development Bank) promoting the adoption of cleaner fuels in transport and industry throughout Latin America.

Project summary: "My project aims to design quantitative tools supporting the process of assessing policy and market approaches, promoting an increased penetration of variable energy resources in the energy matrix. This assessment will be performed based on the economic, reliability, and environmental dimensions of the electric power system, accounting for the benefits of reduced fuel use and emissions, and for the increased capital costs of renewables and the necessary re-dispatching of conventional generators."

You can contact Edgar at edgar.virguez@duke.edu

Tianyu is a PhD student in the department of computer science at Duke. Before coming to Duke, he obtained a BS in mathematics and computer science from the Hong Kong University of Science and Technology. His general research interests are in machine learning and applications of machine learning algorithms. 

Project summary: "With the accumulation of energy data, energy domain problems are in need of more sophisticated machine learning models such as neural networks. However, building neural networks of high quality requires heavy human effort, since different hyperparameter configurations lead to significantly different performances. My project will use a multi-armed bandit approach to efficiently design the architecture of neural networks for energy domain problems such as energy demand prediction." 

You can contact Tianyu at tianyu@cs.duke.edu

About the Energy Data Analytics PhD Student Fellows program

The growth of energy-related data in the last decade has created new opportunities for data-driven exploration of solutions to energy problems. Capitalizing on the opportunities presented by this new wealth of data will require scholars with training in both data science and energy application domains. Yet traditional graduate education is limited in its ability to provide such dual expertise. That's why the Duke University Energy Initiative has established the Energy Data Analytics PhD Student Fellows program, preparing cohorts of next-generation scholars to deftly wield data in pursuit of accessible, affordable, reliable, and clean energy systems. 

Each fellow will conduct a related research project for nine months, working with faculty from multiple disciplines. 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. The scholarship of the first two cohorts of fellows will be highlighted at a symposium at Duke University in spring 2020.

The program, which will support a cohort of four fellows in 2018-2019 and a second cohort of four in 2019-2020, is affiliated with the Energy Data Analytics Lab, a collaborative effort of the Duke University Energy Initiative (which houses it), the Information Initiative at Duke (iiD), and the Social Science Research Institute (SSRI). The 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.) 

The application period for the first cohort of Energy Data Analytics PhD Student Fellows is now closed. Guidelines for applications for the second cohort (2019-2020) will be released in early 2019. 

The growth of energy-related data in the last decade has created new opportunities for data-driven explorations of solutions to energy problems. Capitalizing on the opportunities presented by this new wealth of data will require scholars with training in both data science and energy application domains. Recognizing this—and the fact that traditional graduate training is limited in its ability to provide such dual expertise—this doctoral student fellows program aims to train a cohort of next-generation scholars who, working with faculty from multiple disciplines, will draw on knowledge in energy application areas (e.g., engineering, operations research, economics, policy, or closely related fields) and data science methods (e.g., computer science, mathematics, or closely related fields) to leverage burgeoning sources of energy data to affect the evolution of energy systems and the policies that govern them.

Benefits for Successful Applicants:

  • Funding equivalent to one-half of a full fellowship for an academic year.
  • Conference travel support and data acquisition support up to $2,000.
  • Priority access to virtual machines, storage, and other computational resources.
  • Participation in a symposium in Spring 2020.

Student Eligibility:

  • Any Duke doctoral student currently enrolled full time. The student must be working with (or proposing to work with) two faculty members representing interdisciplinary expertise across both an energy application domain and a data science field.
  • Doctoral student must show evidence of genuine interest in research on important energy data analytics topics that play a role in their larger dissertation objectives.
  • Applicants must be making successful academic progress in their home department.
  • Doctoral students as early as their second year at Duke can apply to be an Energy Data Analytics Fellow during the next academic year.  Preference will be given to students who have completed coursework by the beginning of their fellowship term.

Fellowship Duration:

Energy Data Analytics PhD Student Fellows are expected to work on their proposed project for a minimum of one academic year (9 months). Preference will be given to students who incorporate the proposed project into their dissertation research. The fellowship will begin at the start of the fall semester (September 1) and end the following spring (May 31).  

Application Deadline:

The deadline for application submission is February 28, 2018. Download the application here and submit it as a pdf document to Kyle Bradbury (kyle.bradbury@duke.edu) no later than 11:59 p.m. on the day of the deadline.

Support:

This fellowship program is made possible through 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.

Questions?

Please contact Kyle Bradbury (kyle.bradbury@duke.edu), managing director of the Energy Data Analytics Lab, with any questions about this program.

                                                              

Mailing Address

Duke University Energy Initiative
Box 90467
Durham, NC 27708

Street / Delivery Address

Duke University Energy Initiative
140 Science Drive
Gross Hall, Suite 101
Durham, NC 27708

919-613-1305

 
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  • Lecturing Fellow and Managing Director, Energy Data Analytics Lab

    Kyle brings experience in machine learning and statistical modeling to energy problems.