The recent growth of energy-related data and evolution of data science techniques have created promising new opportunities for solving energy challenges. Capitalizing on these 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.
About the fellows program
Each Energy Data Analytics PhD student fellow conducts 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 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.
Supporting a cohort of four fellows in 2018-2019 and a second cohort of four in 2019-2020, the program is affiliated with the Energy Data Analytics Lab, a collaborative effort of the Duke University Energy Initiative (which houses it), the Rhodes Information Initiative at Duke (Rhodes 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.)
Contact Kyle Bradbury, managing director of the Energy Data Analytics Lab.
Meet Duke University's 2019-2020 cohort of Energy Data Analytics PhD Student Fellows:
Alina is a computer science PhD student working with Professor Cynthia Rudin in the Prediction Analysis Lab. She is interested in identifying buildings with poor insulation to better inform civic planning as well as data concerning efficiency.
Project summary: “A poorly insulated building requires more energy to heat and cool than a well-insulated building. Using publicly available thermal satellite imagery and a super-resolution algorithm, estimates can be made about the energy efficiency of individual buildings. Super-resolution is the problem of finding a high-resolution image from a low-resolution image. The goal is to create high-resolution, accurate, realistic reconstructed images from low-resolution images to enhance image data and recover visual information. By identifying buildings with poor insulation (and therefore poor energy efficiency), policy can be informed by data, such as providing incentives to install new insulation. My goal is for data to detect in-need communities not only by region, but by specific household, to further direct policy and civic planning.”
You can contact Alina at firstname.lastname@example.org.
Bohao Huang is a PhD student in electrical and computer engineering at the Pratt School of Engineering. Working in Duke’s 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.
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, however 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 email@example.com.
Jun is a PhD student in earth and ocean sciences at the Nicholas School of the Environment. Her research models energy systems in the context of trade to better understand international energy security.
Project Summary: “Embodied energy is the sum of energy consumed to produce a commodity and is widely used to assess the total environmental impacts of a production process. My project uses a hybrid input-output approach to model embodied energy flows throughout the global economy. This approach deviates from the existing literature by addressing the feedbacks between energy and non-energy industries rather than simply the use of energy by non-energy industries. This tool will allow for examination of industry requirements for energy transformations, as well as the embodied energy in supply chains, of over 130 countries across 20 years (1995-2015). My goal is to then use machine learning techniques to simulate the model for future impacts.”
You can contact Jun at firstname.lastname@example.org.
Tongshu Zheng is a PhD student in civil and environmental engineering at Duke’s Pratt School of Engineering. He is interested in leveraging data-driven techniques to solve air pollution problems including developing a novel satellite-based remote sensing algorithm to cost-effectively and accurately assess the loss in solar energy production due to particulate matter air pollution.
Project Summary: “Atmospheric particulate matter (PM) can diminish solar energy production through ambient PM and PM deposited on solar panels. Solar energy loss is estimated to be as high as 25–35% in highly polluted areas. My project aims to assess and predict the loss in solar energy production in a low-cost and accurate manner by using image processing techniques and machine learning algorithms to automatically extract information from the global, 3m resolution satellite imagery. I hope toinform solar energy companies about their daily production loss due to PM pollution as well as help them strategize the optimal solar panel cleaning frequency. I want my research to catalyze the development of startup companies that specialize in providing solar production loss data.”
You can contact Tongshu at email@example.com.
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 firstname.lastname@example.org.
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 email@example.com.
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 firstname.lastname@example.org.
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 email@example.com.