Supported by the Alfred P. Sloan Foundation, the Energy Data Analytics Ph.D. Student Fellows program at Duke University readies emerging scholars to apply cutting-edge data science techniques to energy challenges.

The program has recently expanded and is open to doctoral students at Duke University, North Carolina A&T State University, North Carolina State University, University of North Carolina at Chapel Hill, University of North Carolina at Charlotte, and University of North Carolina at Greensboro.

Questions? Contact Trey Gowdy (trey.gowdy@duke.edu), Program Coordinator for the Energy Data Analytics Lab.

About the Fellows Program

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. In 2018, the Duke University Energy Initiative established the Energy Data Analytics Ph.D. Student Fellows program, preparing cohorts of next-generation scholars to deftly wield data in pursuit of accessible, affordable, reliable, and clean energy systems. This program is funded by a grant from the Alfred P. Sloan Foundation.

Each Ph.D. Student Fellow in the program conducts a related research project, working with faculty from multiple disciplines and receiving financial support for 3 months of summer support and $1,500 in research funds for computation and professional development. The fellows take part in regular mentorship and training workshops to improve their understanding of energy systems and data science tools and practices as well as to enhance their skills at collaborating and communicating across disciplines.

The first two cohorts of fellows included Duke University doctoral students from degree programs in civil and environmental engineering, computer science, earth and ocean sciences, electrical and computer engineering, and environmental policy. The program has since expanded to welcome applications from doctoral students at Duke University, North Carolina A&T State University, North Carolina State University, University of North Carolina at Chapel Hill, University of North Carolina at Charlotte, and University of North Carolina at Greensboro.

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). (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 Duke University's 2021 cohort of Energy Data Analytics Ph.D. Student Fellows:

Headshot of Yang Deng - PhD student at Duke
 

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. 

Project: Deep Learning for the Inverse Design of Metasurface-based Energy Materials 
All-dielectric metasurfaces emerged as prominent platforms to manipulate energy at surfaces and have proven their capabilities as highly efficient energy materials. With thermal and waste energy eliminated by tailored emission, all-dielectric metasurfaces provide a revolutionary solution for efficient energy harvesting. However, all-dielectric metasurfaces require extensive numerical simulations to understand their physical properties. Deep learning approaches have been studied on the accelerated inverse design of metasurfaces, yet more complex geometries remain mostly unexplored. I am interested in identifying deep learning solutions that can accurately identify complex all-dielectric metasurface geometry that yields targeted frequency-dependent scatterings. The project’s success should accelerate the inverse design of all-dielectric metasurfaces, offering unprecedented high-performance thermal materials in energy harvesting applications. The deep learning methods can particularly identify optimal ADM thermal emitters ready to deploy in the realization of high-efficiency thermophotovoltaic cells. 

Advisors: Dr. Jordan Malof, Dr. Willie Padilla You can contact Yang Deng at yang.deng@duke.edu.

Headshot of Qian Luo - a PhD student at NC State University
 

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.  

Project: Reducing health impacts from the power sector emissions in China with economic dispatch and energy storage  
China has started a new round of power market reform, introducing a dispatch approach that minimize the electricity generation costs to its current power sector. Although several studies have investigated the carbon emission impacts of adopting this economic dispatch in China, none have estimated the human health impacts brought by this transition. Comprehensively understanding the impacts of the power market reform will provide insights on how to make better regulations to protect the public health. This project will estimate the health impacts by integrating power system models and air quality models, and also explore how to cost-effectively reduce these health impacts by internalizing real-time health costs in plant dispatch decisions and re-optimizing the unit commitment and economic dispatch in light of these impacts.  

Advisors: Dr. John Baugh, Dr. Fernando Garcia Menendez, Dr. Jeremiah Johnson. You can contact Qian Luo at qluo4@ncsu.edu.

Headshot of Suhas Raju - PhD student at UNC Charlotte
 

Suhas Raju is a Ph.D. student in electrical engineering at the University of North Carolina 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. 

Project: Exploration of Neural Networks and Kalman filters to estimate parameters driving energy use.  
The buildings sector accounts for approximately 33% of worldwide greenhouse gas emissions and more than 40% of primary energy usage. Over the past 3 years we have analyzed over 6,000 small commercial buildings and identified issues driving high energy usage. Achieving only a 1% reduction in each of those 6,000 facilities would represent the removal of about 380 residential homes from the grid. The work in this project will focus on developing appropriate artificial intelligence approaches to leverage this large data set to detect reasons for high energy consumption. 

Advisors: Dr. Ahmed Arafa, Dr. Robert Cox. You can contact Suhas Raju at ssuhasra@uncc.edu.

Headshot of Josh Randall - a PhD student at NC State University
 

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. 

Project: Scales of Energy Poverty: Understanding Energy Justice Through Spatial Analysis 
Nearly 1.4 million North Carolinians and 4 billion people around the world experience energy poverty - they are unable to both secure energy and acquire other basic human needs. Many community agencies, policy programs, and research studies identify energy poverty through the use of geospatial technologies and data. However, these efforts can fail to recognize many who need assistance due to the aggregation of spatial data to inappropriate scales. Because energy poverty is felt at a household level, aggregation to a large spatial scale (a county, for example) effectively hides forms of energy poverty, leading to many not receiving the type of help they need. My work will provide a suite of data analyses that identify spatial patterns of energy poverty hidden when data is aggregated answering who is missed and where. My goal is to use these outcomes to inform policymakers on the equitable outcomes of potential interventions, while also community leaders with information to combat energy poverty. 

Advisors: Dr. Christopher Galik, Dr. Jelena Vukomanovic. You can contact Josh Randall at jnrandal@ncsu.edu.

Headshot of Ben Ren - PhD student at Duke University
 

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. 

Project: Mapping energy infrastructure with UAVs and deep learning 
As energy systems undergo a dramatic transition to more renewable and distributed energy generation, energy security in the forthcoming decades will depend heavily upon increasingly sophisticated energy systems modeling and effective decision-making.  Success in these endeavors depends crucially upon access to high-quality and detailed information about existing energy infrastructure: e.g., residential solar systems and their capacity, transmission/distribution lines, and more. Unfortunately, however, such information is often limited, incomplete, or only accessible for a substantial fee. In this work, I propose to overcome this obstacle to energy decision-making by leveraging recent breakthroughs in machine learning to develop algorithms that can automatically extract energy information from high-resolution imagery from unmanned aerial vehicles (UAVs).  In recent years such algorithms have been successfully demonstrated for collecting energy information on satellite imagery, however, many types of energy infrastructure cannot be reliably identified or characterized using satellite imagery due to its limited resolution.  In this work, I propose to collect UAV imagery of several types of energy infrastructure and demonstrate that these objects can be identified and characterized with unprecedented accuracy.  This technique, if effective, will provide a powerful new tool that will facilitate energy modeling and decision-making, and help ensure energy security in the forthcoming decades.     

Advisors: Dr. Kyle Bradbury, Dr. Jordan Malof. You can contact Ben Ren at simiao.ren@duke.edu.

Headshot of Celine Robinson - a PhD student at Duke University
 

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 natech risk in complex and interconnected systems.   

Project: Detection of Above Ground Storage Tanks Using Faster R-CNN 
Energy systems are essential for society's functioning and economy; however, they are susceptible to natural hazard-induced failures. Extreme hydrological events can cause above ground storage tank (ASTs) floatation, bucking, and sliding failures, resulting in chemical releases that can migrate offsite posing threats to surrounding communities. Unfortunately, infrastructure fragility assessments, which required AST location, type, and volume, have been constrained by limited, incomplete, or inaccessible data. The increasing abundance of high-resolution overhead imagery collected through the National Agriculture Imagery Program (NAIP) may provide a valuable information source.  I propose to utilize deep learning methods to create multi-class object detection models and new above ground storage tank datasets for use in natural hazard risk assessment. 

Advisors: Dr. Mark Borsuk, Dr. David Carlson. You can contact Celine Robinson at celine.robinson@duke.edu.

Headshot of Zhenxuan - a PhD student at Duke University.
 

Zhenxuan is a Ph.D. student in the University Program of Environmental Policy at Duke University with an economics concentration. Lying 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. 

Project: Using Machine Learning Approach to Improve the Understanding of Household Adoption of Energy-Efficient Technologies 
The rapid growth in world energy consumption brings increased local air pollution and greenhouse gas emissions. Energy-efficient technologies can create substantial economic and environmental benefits by saving the financial costs and mitigating the environmental damages induced by rising energy consumption. Household adoption rate of these cost-effective technologies, however, is still low especially in developing countries due to several market failures and barriers. The increasing availability of big data on consumer choices provides both opportunities and challenges for studying household preferences for energy-efficient technologies. Using a comprehensive air conditioner transaction dataset, my project will develop an empirical method by combining machine learning approach with economic modeling of consumer demand to better understand household adoption of energy-efficient products and improve the out-of-sample prediction of their choices. 

Advisors: Dr. Billy Pizer, Dr. Cynthia Rudin. You can contact Zhenxuan Wang at zhenxuan.wang@duke.edu.

Meet Duke University's 2019-2020 cohort of Energy Data Analytics Ph.D. Student Fellows:

PhD student AlinaAlina is a computer science Ph.D. 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 alina.barnett@duke.edu.

PhD Student BohaoBohao Huang is a Ph.D. 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 bohao.huang@duke.edu.

PhD Student JunJun is a Ph.D. 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 jun.shepard@duke.edu.

Tongshu Zheng is a Ph.D. 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 to inform 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 tongshu.zhen@duke.edu.

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

PhD Student BohaoBohao Huang is a Ph.D. 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 bohao.huang@duke.edu.

Energy Data Analytics PhD fellow-Qingran LiQingran 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

Energy Data Analytics PhD fellow-Edgar VirguezEdgar 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

Energy Data Analytics PhD fellow-Tianyu WangTianyu is a Ph.D. 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.

Questions?

Contact Trey Gowdy, Program Coordinator for the Energy Data Analytics Lab

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 

Aug
06
Location: Virtual
Time: 10:00 am to 3:00 pm
  • Assistant Research Professor, Pratt School of Engineering & Managing Director, Energy Data Analytics Lab

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