A team of students in conjunction with Duke’s Office of Information Technology will make use of Duke’s wireless network data to build detailed maps of wireless coverage, strength and utilization across campus.  The data will be overlayed on a campus map of buildings, and used to analyze trends in wireless demand (e.g. areas that need additional coverage or bandwidth), trends in wireless utilization (e.g. where and what times are the wireless network used the most), identify underutilization for resource reallocation, and trends in how groups of people move around campus.  Students will work directly with the network data and have access to the analytics tools used in OIT, and will have a great opportunity for exploration of the data in consultation with OIT network, security and data analytics professionals.

A team of students led by Professors Jonathan Mattingly and Gregory Herschlag will investigate gerrymandering in political districting plans.  Students will improve on and employ an algorithm to sample the space of compliant redistricting plans for both state and federal districts.  The output of the algorithm will be used to detect gerrymandering for a given district plan; this data will be used to analyze and study the efficacy of the idea of partisan symmetry.  This work will continue the Quantifying Gerrymandering project, seeking to understand the space of redistricting plans and to find justiciable methods to detect gerrymandering. The ideal team has a mixture of members with programing backgrounds (C, Java, Python), statistical experience including possibly R, mathematical and algorithmic experience, and exposure to political science or other social science fields.

Read the latest updates about this ongoing project by visiting Dr. Mattingly's Gerrymandering blog.

Would you like to know what influences patients’ medical decisions when outcomes are uncertain? Using a big data approach, we will explore a large number of physician-patient conversations and disentangle the complex decision-making process.  Students will be introduced not only to data science but also to behavioral research and aspects of communication in healthcare. This work will inform physicians on how to reduce overutilization of unnecessary interventions and ensure the well-being of patients.

A team of students led by researchers in the Center for Health Policy and Inequalities Research will develop a platform that visualizes significant life events across time for more than 3,000 orphaned and separated children in Cambodia, Ethiopia, India, Kenya, and Tanzania from the Positive Outcomes for Orphans (POFO) study. The types of life events visualized on the timeline will include: the death of a parent, changes in living locations, school levels achieved, special events, traumatic events, and reported wellbeing at different ages. This data will be displayed via mobile devices and will serve to allow the participant to visualize and verify the information provided about their lives. Ultimately, the platform will allow researchers to ensure accuracy of the data provided and also allow greater audiences to visualize the individuality of the study's aggregate data.

A team of students led by UNC-CH graduate student Grant Glass and Duke English professor Charlotte Sussman will track the thousands of Daniel Defoe’s Robinson Crusoe editions – including the plethora of movies and “Robinsoniades,” most of which are deviations from Defoe’s original work. By examining the differences in these stories –through word-vector models and categorization algorithms, we can trace how the deviations often reflect the place and time of their production and consumption, evoking a range of questions that further our understanding of how the expanse and collapse of the British Empire is wrapped up in notions of capitalism, race, empire, gender, and climate concerns. Along the way, we will examine questions of intellectual property, piracy, and authorship as they relate to both the 18^th century and today.

A team of students will work with Duke's Office of Development & Alumni Affairs to understand how cutting-edge data analytic techniques, such as sentiment analysis and network analysis, can be used to understand a variety of giving behaviors and trajectories. Students will work with de-identified data in a secure computing environment, and will have a rich opportunity for creative exploration in consultation with Development professionals.

Maddie Katz (Global Health and Evolutionary Anthropology Major), Parker Foe (Math/Spanish, Smith College), and Tony Li (Math, Cornell) spent ten weeks analyzing data from the National Transgender Discrimination Survey. Their goal was to understand how the discrimination faced by the trans community is realized on a state, regional, and national level, and to partner with advocacy organizations around their analysis.

Luke Raskopf, PoliSci major and Xinyi (Lucy) Lu, Stats/CompSci major, spent ten weeks investigating the effectiveness of policies to combat unemployment and wage stagnation faced by working and middle-class families in the State of North Carolina. They worked closely with Allan Freyer at the North Carolina Justice Center.

Xinyu (Cindy) Li (Biology and Chemistry) and Emilie Song (Biology) spent ten weeks exploring the Black Queen Hypothesis, which predicts that co-operation in animal societies could be a result of genetic/functional trait losses, as well as polymorphism of workers in eusocial animals such as ants and termites. The goal was to investigate this idea in four different eusocial insect species.

Matthew Newman (Sociology), Sonia Xu (Statistics), and Alexandra Zrenner (Economics) spent ten weeks exploring giving patterns and demographic characteristics of anonymized Duke donors. They worked closely with the Duke Alumni Affairs and Development Office, with the goal of understanding the data and constructing tools to generate data-driven insight about donor behavior.

Over ten weeks, Computer Science Majors Amber Strange and Jackson Dellinger joined forces with Psychology major Rachel Buchanan to perform a data-driven analysis of mental health intervention practices by Durham Police Department. They worked closely with leadership from the Durham Crisis Intervention Team (CIT) Collaborative, made up of officers who have completed 40 hours of specialized training in mental illness and crisis intervention techniques.

Lauren Fox (Cultural Anthropology) and Elizabeth Ratliff (Statistics, Global Health) spent ten weeks analyzing and mapping pedestrian, bicycle, and motor vehicle data provided by Durham's Department of Transportation. This project was a continuation of a seminar on "ghost bikes" taught by Prof. Harris Solomon.

Over ten weeks, Math/CompSci majors Benjamin Chesnut and Frederick Xu joined forces with International Comparative Studies major Katharyn Loweth to understand the myriad academic pathways traveled by undergraduate students at Duke. They focused on data from Mathematics and the Duke Global Health Institute, and worked closely with departmental leadership from both areas.

Zijing Huang (Statistics, Finance), Artem Streltsov (Masters Economics), and Frank Yin (ECE, CompSci, Math) spent ten weeks exploring how Internet of Things (IoT) data could be used to understand potential online financial behavior. They worked closely with analytical and strategic personnel from TD Bank, who provided them with a massive dataset compiled by Epsilon, a global company that specializes in data-driven marketing.

Over ten weeks, Public Policy major Amy Jiang and Mathematics and Computer Science major Kelly Zhang joined forces with Economics Masters student Amirhossein Khoshro to investigate academic hiring patterns across American universities, as well as analyzing the educational background of faculty. They worked closely with Academic Analytics, a provider of data and solutions for universities in the U.S. and the U.K.

Anne Driscoll (Economics, Statistical Science), and Austin Ferguson (Math, Physics) spent ten weeks examining metrics for inter-departmental cooperativity and productivity, and developing a collaboration network of Duke faculty. This project was sponsored by the Duke Clinical and Translational Science Award, with the larger goal of promoting collaborative success in the School of Medicine and School of Nursing.

Students in the Performance and Technology Class create a series of performances that explore the interface between society and our machines. With the theme of the cloud to guide them, they have created increasingly complex art using digital media, microcontrollers, and motion tracking. Their work will be on display at the Duke Choreolab 2016.

This project transforms an inaccessible audio archive of historic North Carolina folk music colllected by Frank Clyde Brown in the 1920s-40s into a vital, publicly accessible digital archive and museum exhibition. 

Nonnegative matrix factorization (NMF) has an established reputation as a useful data analysis technique in numerous applications. However, its usage in practical situations is undergoing challenges in recent years.The fundamental factor to this is the increasingly growing size of the datasets available and needed in the information sciences. To address this, in this work we propose to use structured random compression, that is, random projections that exploit the data structure, for two NMF variants: classical and separable. In separable NMF (SNMF) the left factors are a subset of the columns of the input matrix. We present suitable formulations for each problem, dealing with different representative algorithms within each one.

The goal of this project is take a large amount of data from the Massive Open Online Courses offered by Duke professors, and produce from it a coherent and compelling data analysis challenge that might then be used for a Duke or nation-wide data analysis competition.