Duke University, Department of Mathematics

Program ID: Duke-DATA2019 [#763]
Program Title: Data+ 2019
Program Type: Undergraduate program
Program Location: Durham, North Carolina 27708-0320, United States [map]
Subject Areas: Data Science, Interdisciplinary
Application Deadline: 2019/02/25 11:59PMhelp popup finished (2018/12/08, finished 2019/09/01)
Program Description:    

*** this program has been closed and new applications are no longer being accepted. ***

Data+ is a full-time ten week summer research experience that welcomes Duke undergraduate and masters students interested in exploring new data-driven approaches to interdisciplinary challenges. It is suitable for students from all class years and from all majors.

Students join small project teams (at most 3 undergrads and 1 masters per team), working alongside other teams in a communal environment. They learn how to marshal, analyze, and visualize data, while gaining broad exposure to the modern world of data science. The projects (see below) come from an extremely diverse set of subject areas. It is our hope that students will be able to both work deeply into their specific project and get a very broad picture of most of the skills needed for modern data science.

Participants will receive a $5,000 stipend, out of which they must arrange their own housing and travel . Funding and infrastructure support are provided by a wide range of departments, schools, and initiatives from across Duke University, as well as by outside industry and community partners. Participants may not accept employment or take classes during the program; this requirement is strictly enforced and non-negotiable..

The program runs from May 28th until August 2nd, 2019. The application deadline is Feb. 25, 2019, but we will evaluate applications on a rolling basis, so please get your applications in as soon as you can!

You will find the projects planned for summer 2019 in the numbered list below. Click on the project names to learn more. Please indicate the number of the projects you choose when you apply; you may list up to three choices in ranked order of preference. If you are seeing this page in December 2018, please note that more projects may be added in the coming weeks; there will be eventually be approximately 30 projects listed!

Due to the nature of the data involved in some of the projects, human subjects research training will be required of all participants and will be provided after admission to the program. With each project, we have attempted to list potential majors and/or interests that might be most interested in the project, but these should not be seen as requirements in any way! Quantitative STEM majors like mathematics, computer science, statistics, and electrical engineering are relevant to all.


1) Neuroscience in the Courtroom Neuroscience evidence is increasingly being used in criminal cases to explain criminal behavior and lessen responsibility. A team of students led by researchers within the Science, Law, and Policy Lab will explore a national set of criminal cases in which neuroscience evidence is used to see what aspects of the criminal trial (i.e., offense, age of offender, etc.) may predict the outcome of future cases. Psychology, Neuroscience, Law, Public Policy, History, all quantitative STEM.

2) Identifying extreme events in wholesale energy markets A team of students will explore a variety of methods for identifying and characterizing price shocks in the energy market. They will have the opportunity to consult with professionals from Modern Energy Group (MEG), which finances and operates various distributed energy resources operating in wholesale energy markets, ranging from solar panels to residential smart thermostats. Economics, Energy and Environment, Finance, all quantitative STEM.

3) Remembering the Middle Passage A team of students will use a variety of data sets and mapping technologies to determine a feasible location for a deep-sea memorial to the transatlantic slave trade. Led by English professor Charlotte Sussman, in association with the Representing Migrations Humanities Lab, this team will create a new database that combines previously-disparate data and archival sources to discover where on their journeys enslaved persons died, and then to visualize these journeys. English, Literature, History; African and African American studies, Philosophy, Art history, Visual and media studies, Geography, all quantitative STEM.

4) Durham Evictions A team of students will explore and develop means of using evictions data to drive meaningful policy change that help Durham residents stay in their homes. They will have the opportunity to consult with professionals from DataWorks NC and the Eviction Diversion Program. Public policy, Sociology, History, Law, Economics, Political Science, all quantitative STEM.

5) Network Visualization of IoT Devices A team of students will make use of Dukes network traffic data to perform IoT device behavioral fingerprinting that can be employed to identify device types. The team will have the opportunity to consult with security, network, and data professionals from Duke's Office of Information Technology (OIT). all quantitative STEM.

6) Saltwater Intrusion on Coastal Ecosystems Saltwater intrusion and sea level rise are issues of serious concern for people throughout the coastal plain. A team of students will collaborate with researchers to create an interactive data visualization platform, along with educational website content, that compiles remotely sensed estimates of vegetation change throughout the coastal plain and links this data with field salinity estimates. Energy and the environment, biology, education, all quantitative STEM.

7) Big Data for Reproductive Health (II) A team of students led by faculty and researchers from the School of Medicine, the Center for Global Reproductive Health at the Duke Global Health Institute, and the Duke Evidence Lab will collaborate on the user interface for a tool developed to help advocates and policymakers target family planning resources to key populations in low resource populations. This builds off the efforts of previous Data+ and Bass Connections teams. Public policy, global health, pre-med, gender and women's studies, political science, economics, sociology, all quantitative STEM.

8) Security Threat Intelligence Analytics A team of students will learn to analyze threat intelligence data to identify trends and patterns of attacks. The team will consult closely with OIT and Duke's IT Security Office to analyze the type of attacks targeting Duke and other universities. all quantitative STEM.

9) Urodynamic Data and Machine LearningA team of students led by Drs. Aquino (Engineering) and Routh (Urologic Surgery) will develop objective algorithms in order to guide data interpretation from a urology test, known as urodynamics, which is used in children with spina bifida in order to define a patients risk of debilitating bladder and kidney complications. pre-med, biology, all quantitative STEM.

10) Deep Learning and Energy Access Decisions A team of students, led by researchers from the Energy Data Analytics Lab and the Sustainable Energy Transitions Initative, will explore how to develop machine learning techniques for analyzing satellite imagery data and identifying energy infrastucture that can be trained once and applied almost anywhere in the world. Energy and the environment, economics, all quantitative STEM.

11) Breaking the Bundle A team of students partnering with Duke University Libraries will explore the complicated decision space of electronic journal licensing. The team team will model journal purchasing and explore software to assist the library in its decison-making efforts. Economics, law, library science, all quantitative STEM.

12) Athletic Injury Risk Assessment A team of students led by researchers from the Michael W. Krzyzewski Human Performance Laboratory (K-Lab) will develop an analytic and report generating web-based application to help the K-Lab reduce musculoskeletal injuries in student-athletes at Duke University. Pre-med, sports science, biology, all quantitative STEM.

13) U.S Ambivalence towards Profits A team of students led by history professor Sarah Deutsch will do data mining in newspaper and Congressional databases to investigate the dynamics behind the excess profits tax laws Congress passed between 1918 and 1948 and the concept of price gouging which continues to shape legislation today. History, Economics, Political Science, Sociology, all quantitative STEM.

14) Duke Building Energy Use Report A team of students will review troves of utility usage data and attempt to build an attractive and practical monthly energy use report for every building and school at Duke. This report will not only show historical usage but also develop an energy benchmark for comparison and conservation tips for local administrators to take action. Energy and the Environment, Economics, all quantitative STEM.

15) Human Rights in the Postwar World A team of students will analyze how U.S. mass mediaparticularly newspapersenlist text and imagery such as press photographs to portray genocide, human rights, and crimes against humanity from World War II to the present. English, Philosophy, Visual and Media Studies, Political Science, all quantitative STEM.

16) Recidivism in Durham County Jails A team of students will collaborate with Durhams Crisis Intervention Team, a group of law enforcement, fire, and EMS personnel who are specially trained to interact with citizens in mental health crisis. The team will analyze data from the Durham County Jail to track repeat arrests by persons with or without mental illness, along with their use of mental health and other services in the Duke Health System. Public Policy, Law, all social sciences, all quantitative STEM.

17) StreamPulse A team of students led by faculty and students in Duke's River Center will manipulate, model and visualize time series data derived from hundreds of rivers throughout the world. Students will gain experience working with large datasets derived from environmental sensors and will be able to direct the data project based on their learning interests. Energy and the Environment, Ecology, Biology, all quantitative STEM.

18) Speech Emotion Analysis A team of students led by statistics professor Jie Ding from the University of Minnesota will develop algorithms to recognize human emotions (e.g. calm, happy, angry, etc.) from audio speech data, and to incorporate new emotions into an existent speech. Psychology, all quantitative STEM.

19) Invisible Adaptations: from Hamlet to the Avengers A team of students will develop means to track the movement of adaptations within contemporary culture through machine learning techniques. Students will identify features of different master narratives, which will be used to demonstrate how certain stories are modified and retold over and over again. English and Literature, all quantitative STEM.

20) American Predatory Lending A team of students led by researchers in the Global Financial Markets Center at Duke Law will collect and analyze home mortgage market data that was publicly available during the run-up to the Financial Crisis (1997 2007). Analyzing and presenting this data will allow the team to understand what information was publicly available to policymakers preceding the Crisis. Finance, Economics, Public Policy, all quantitative STEM.

21) Visualizing the Nation's Water Quality Data A team of students led by Jim Heffernan, Nick Burns, and partners at UNC and EPA will create interactive data visualizations of water quality data in rivers and lakes of the United States. These tools will aid environmental scientists, managers, policy-makers, and students who want to investigate patterns of water pollution across broad scales of space and time. Energy and the Environment, Public Policy, all quantitative STEM.

22) Basketball Analytics Pipeline Students on this project will create a a cohesive data pipeline for generating, modeling, and visualizing basketball data. In particular, the team will understand how to extract data from freely available video, how to model such data to capture player efficiency, strength and leadership, and how to visualize such data outcomes.

23 Smart Meters and Electricity Theft A team of students led by researchers in the Energy Access Project will develop means to evaluate non-technical electricity losses (theft) in developing countries through machine learning techniques applied to smart meter electricity consumption data. Energy and the Environment, all quantitative STEM.

24 Illuminating Responses of Lakes to Stressors Data-enabled approaches present new opportunities to analyze responses of aquatic ecosystems to stressors and to illustrate scientific findings in new formats that are more widely accessible. A team of students will create a web-based storytelling platform that illustrates the results of freshwater ecosystem studies conducted at the IISD-Experimental Lakes Area in Canada.

25 Investigating Oil and Gas Production in the United Kingdom Producing oil and gas in the North Sea, off the coast of the United Kingdom, requires a lease to extract resources from beneath the ocean floor and companies bid for those rights. This team will consult with professionals at ExxonMobil to understand why these leases are acquired and who benefits. Energy and the Environment, Economics, Finance, all Quantitative STEM.

26 Exploring Oil and Gas Production in the Gulf of Mexico Producing oil and gas in the Gulf of Mexico requires rights to extract these resources from beneath the ocean floor and companies bid into the market for those rights. The top bids are sometimes significantly larger than the next highest bids, but its not always clear why this differential exists and some companies seemingly overbid by large margins. This team will consult with professionals at ExxonMobil to curate and analyze historical bid data from the Bureau of Ocean Energy Management that contains information on company bid history, infrastructure, wells, and seismic survey data as well as data from the companies themselves and geopolitical events. Energy and the Environment, Economics, Finance, all Quantitative STEM.

27) Getting Granular on Social Determinants of Health Social and environmental contexts are increasingly recognized as factors that impact health outcomes of patients. This team will have the opportunity to collaborate directly with clinicians and medical data in a real-world setting. They will examine the association between social determinants with risk prediction for hospital admissions, and to assess whether social determinants bias that risk in a systematic way. his Data+ project is sponsored by the Forge, Duke's center for actionable data science. pre-Med, all social sciences, all quantitative STEM.

Application Materials Required:
Submit the following items online at this website to complete your application:
And anything else requested in the program description.

Further Info:
Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320

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