Research

Research projects at Rhodes iiD focus on building connections. We encourage crosspollination of ideas across disciplines, and to develop new forms of collaboration that will advance research and education across the full spectrum of disciplines at Duke. The topics below show areas of research focus at Rhodes iiD. See all of our research.

Baboon Reproduction: Linking Sexual Swellings, Estrogen, and Mating Behavior

Introduction

Female baboons occasionally exhibit large swellings on their behinds. Although these ‘sexual swellings’ may evoke disgust from human on-lookers, they provide important information to group members about a female’s reproductive state. To figure out what these sexual swellings mean and whether male baboons notice, we need to look at the data.  

This data expedition explores the relationship between female baboon sexual swellings, female estrogen concentrations, and male mating success. The expedition uses long-term data collected on wild baboons by the Amboseli Baboon Research Project. After learning background information about baboon social lives and reproduction, students generate testable predictions for two hypotheses about baboon reproduction. Students then learn how to use the popular R packages dplyr and ggplot2 to calculate descriptive statistics about the dataset. Finally, students perform data visualization to understand and explore patterns in animal mating behavior and sexual signals.

Learning objectives

  • Learn basics of exploratory data analysis (descriptive statistics, generating plots) in R
  • Learn basics of popular R packages dplyr and ggplot2
  • Increase understanding of association between hormones and mating behavior
  • Increase science literary skills (e.g. generating predictions, interpreting results)

Course Materials

Workflow

The lesson started with a brief Powerpoint presentation to introduce the class to basic information on baboon sociality and reproduction. At the end of the Powerpoint, students were introduced to 2 key hypotheses about baboon reproduction that they then explored using R. The class was divided into small groups where students worked together to propose possible predictions to test these hypotheses (and filled out the first section of the provided worksheet) before seeing the provided predictions.

Students then worked through the provided R script and accompanying dataset to test these predictions. Students with prior experience with R were able to skip ahead by following instructions on the R script, while most of the class worked through the script step-by-step with guidance from instructors. The course instructors walked the students through most of the script, then let students work independently to complete Data Visualization Part 2. Students filled out the worksheet as they went along.

At the end of the R script, students ultimately replicated Figure 1 from Gesquiere et al. 2007. This figure is included as the final slide of the Powerpoint presentation. The end of the class session was used to interpret the figure and discuss how it relates to the 2 project hypotheses.

Student level

This lesson is designed for undergraduate students who have little to no exposure to R or other programming software. It could be easily adjusted for students who are familiar with R or other programming software. This lesson takes about 75 minutes to complete.

The dataset and the Amboseli Baboon Research Project

The dataset for this expedition is a subset of the long-term database of the Amboseli Baboon Research Project, a project co-directed by Drs. Jeanne Altmann, Susan Alberts, Beth Archie, and Jenny Tung. The Amboseli Baboon Research Project has collected demographic, behavioral, genetic, and endocrinological data on a population of wild baboons since 1971 in order to study questions related to animal behavior, life history, behavioral ecology, genetics, and physiology. The project’s database is managed by Jake Gordon at Duke University and Niki Learn at Princeton University.

The unit of analysis for this dataset is a fecal estrogen sample from a cycling1 female. For each fecal sample (n = 843), 6 variables are recorded:

  1. female - identity of the female baboon
  2. cyle_day - day of her reproductive cycle
  3. estrogen - fecal estrogen concentration
  4. swelling_size - sexual swelling size2
  5. alpha_consort - whether or not the female consorted3 with an alpha male4 on that day
  6. nonalpha_consort - whether or not the female consorted with a non-alpha male on that day

This dataset includes data from 93 female baboons, with approximately 10 fecal estrogen samples per female. Minor differences between this dataset and the the dataset used in Gesquiere et al. 2007 are due to small, incremental changes in the database over time.

Footnotes

1 cycling: sexually mature but not pregnant or lactating
2 female yellow baboons exhibit exaggerated sexual swellings (an enlargement/engorgement of the genital and perineal skin) around ovulation
3 consortship: a period in which a male mate-guards a female. Virtually all matings and conceptions occur during consorts

Deep Learning with Aerial Wildlife Surveillance

We led a 75-minute class session for the Marine Mammals course at the Duke University Marine Lab that introduced students to strengths and challenges of using aerial imagery to survey wildlife populations, and the growing use of machine learning to address these "big data" tasks.

Social Network Analysis Basics: Case Studies from Game of Thrones and the National Hockey League

The goal of this Data Expedition was to introduce students to the exploration of social networks data using R. Students learned to load and plot a social network in R and then perform some basic analyses on two different networks: Hockey Fights in the National Hockey League in 2018-2019 and characters in Game of Thrones Season 3. Students used social network analysis to better understand who is connected to whom, how frequently they interact, and how they are interacting.

Text Analysis of Political Speech

The data that students see in their statistics courses are often constrained to numeric and tabular data. However, there is an exciting field of data science and statistics known as text analysis. This expedition introduces students to the concept of treating text as data frames of words, and demonstrates how to perform basic analyses on bodies of text using R. Tweets of four Democratic candidates for the 2020 Primary are used as data, and demonstrated text analysis techniques in the expedition include comparisons of word frequencies, log-odds ratios for word usage, and pairwise word correlations.

Neural Network-Based Self-Adjusting Computational Processors

This project aims to improve the computational efficiency of signal operations, e.g., sampling and multiplying signals. We design machine learning-based signal processing modules that use an adaptive sampling strategy and interpolation to generate a good approximation of the exact output. While ensuring a low error level, improvements in computational efficiency can be expected for digital signal processing systems using the implemented self-adjusting modules.

Project Leads: Yi Feng, Vahid Tarokh

 

Click here to view the project team's poster

 

Watch the team's final presentation (on Zoom) here:

 

Computational Approaches to the History of Cartography

Mapping History has focused on the categorizing, labelling, digitization, and 3D reconstruction of 16th & 17th century maps & atlases of London and Lisbon. Over the course of the summer, the Mapping History team has developed its own unique analytical dataset by painstakingly labelling every element contained within these maps, used python to digitize this dataset, and, now in the projects final stage, has begun the process of reconstructing these historical perspectives in a 3D game engine.

Project Lead: Philip Stern, Ed Triplett

Project Manager: Sam Horewood

 

View the team's final poster here

Watch the team's final presentation (on Zoom) below:

Data Science for Retention of College Women in Tech

For our Data+ project, we partnered with Rewriting the Code (RTC), a non-profit organization committed to empowering and fostering a community of college women with a passion for technology. We developed company and industry profiles for the recruitment process that included information ranging from interview and offer rate to negotiation success of salary and benefits. Additionally, we conducted text-analysis of resumes to understand the structure of an optimal tech resume and utilized linear regression to determine the influence of different variables such as ethnicity or college rank.

Project Lead: Sue Harnett

Faculty Lead: Alexandra Cooper

Project Manager: Imari Smith

 

Watch the team's final presentation (on Zoom) here:

AI in the Investment Office

Our team used artificial intelligence to help Duke University Management Company (DUMAC) operate more efficiently by building a cost optimization tool and analyzing and visualizing venture capital data. In our first project centered around cost optimization, we designed a Python script that suggests optimal cash transfers between prime brokers. In our second project, we utilized web scraping and Tableau to aid DUMAC in understanding the relationship between company age and length of current investments.

Project Lead: Robert McGrail, DUMAC

Project Manager: Yi Wang

 

Click here to view the team's final summary

 

Watch the team's final presentation (on Zoom) here:

Applying Security Orchestration, Automation & Response (SOAR) to security threat hunting with Duke’s ITSO

We utilize elements of data science and analysis in order to scour weblogs for potential malicious attacks on Duke’s servers. Additionally, we seek to identify patterns within the data that could be indicative of malicious intent and hope to apply these to real-time data.

Project Leads: Phillip Batton, Nick Tripp

Project Manager: Joao Alberto Capanema Mansur

 

Click here to view the team's project summary slides

 

Watch the team's final presentation (on Zoom) here:

Predictive Churn Models for Duke Season Ticket Holders and Annual Donors

Predictive Churn Models for Duke Season Ticket Holders and Annual Donors is centered around understanding which annual donors are most likely to churn, i.e. not donate the following year. To solve this problem the team built different models to predict the profiles and timing of donor churn. The team made use of Duke Athletics’ internal data supplemented by external data to build predictive machine learning models.

Project Leads: John Haws, Larry Cleaver

Project Manager: Andrew Carr

 

Click here to view the team's final project summary

 

Watch the team's final presentation (on Zoom) here:

Piloting an Environmental Public Health Tracking Tool for North Carolina

Our team members have spent the summer working with the North Carolina Division of Public Health Occupational and Environmental Epidemiology Branch to build a pilot environmental public health data dashboard, with the hope that the pilot tool will be used in DPH’s grant proposal to the CDC for a fully-funded tool. The pilot tool, which is a Tableau dashboard, displays population, health, and environmental data for North Carolina counties and census tracts. The project involved data processing in R, the creation of a detailed metadata table, and building interactive visualizations Tableau.

Project Leads: Mike Dolan Fliss, Kim Gaetz

Project Manager: Melyssa Minto

 

Click here to view the team's final project poster

 

Watch the team's final presentation (on Zoom) here:

American Predatory Lending and the Global Financial Crisis (Year 2)

Carrying forward the work of a 2019-20 Bass Connections team, our Data+ team has worked to better understand the state of the home mortgage market leading up to the financial crisis. The team has built a more in-depth analysis of North Carolina to understand its different regions. We have also expanded the scope of the analysis developing a quantitative portrait on the state of the mortgage market in Arizona, Florida, Massachusetts, Georgia, and Ohio, creating visualization devices for different mortgage market statistics.

 

Project Lead:  Lee Reiners

 

 

Project Manager: Eric Autry

 

Click here to view the team's project poster

 

 

Watch the project team's final presentation (over Zoom) below:

 

 

Linking Urban Land Use to Aquatic Metabolism Regimes

Our group aims to reveal the effects of urban and agricultural land use on metabolic productivities of rivers through statistical manipulation and visualization. During this summer, we classified sites and conducted covariate analyses based on patterns of metabolism, and produced reproducible code that can be used by researchers with similar research goals. We hope that our findings would suggest hypotheses of how disruption is caused by land development, and what factors should land planners avoid introducing.

Project Leads: Jim Heffernan, Phil Savoy

 

Click here to view the team's final poster

 

Watch the team's final presentation here:

Predictive Modeling of Mechanical Failures at Sea

Our team used years of unanalyzed data in a cloud computing environment to conduct exploratory data analysis using natural language processing techniques, as well as visualizations, for Fleet Management Limited. Through this, and preliminary predictive modelling, we hope to help management decrease the number of preventable incidents as each one costs FML more than $10,000.

 

Faculty Sponsor: Paul Bendich

 

Project Manager: Anil Ganti

 

Client Lead: Shah Irani, Fleet Management Limited

 

Click here to view the proect team's poster

 

Watch the team's final presentation (on Zoom) below:

Finding Space Junk with the World’s Biggest Telescopes

Astronomers from the Dark Energy Survey rely on images of deep space to understand the nature of the universe, but these images are often polluted with "space junk": asteroids, comets, satellites, or other objects from our own solar system obstructing the telescope's view. In order to perform their analysis, scientists must first manually identify and mask out such objects from images, a time-consuming process. With leads Michael Troxel, Dan Scolnic, and Chris Walter, we've leveraged deep learning-based computer vision techniques to build models to automatically identify and localize space junk in deep space imagery. 

Project Lead: Dan Scolnic, Michael Troxel, Chris Walter

 

Click here to view the team's final project

Forecasting campus energy usage for improved energy management

In collaboration with Data and Analytics Practice at OIT, our team has completed a series of critical analyses aiding Duke Facilities Management in further optimizing campus energy usage. Data cleaning tools, imputation techniques, and a variety of time series prediction methods ranging from autoregressive models to deep learning networks have been seamlessly integrated into a single interactive forecasting application allowing collaborators to provide accurate and comprehensive utility usage estimates.

Projects Leads: John Haws, Gagandeep Kaur

Project Manager: Billy Carson

 

Click here to view the project team's final poster

 

Watch the team's final presentation (on Zoom) here:

Race and Housing in Durham over the Course of the 20th Century

Our team examined the relationship between race and home values across several units of analysis (household, address, HOLC rating area, census block, block group, and tract) in Durham, NC. We combined data from the decennial censuses (1940-2010), American Community Survey (2005-2018), Durham County Register of Deeds (1997-2020), and Durham County Tax Administration (1997-2021). We find that home values are strongly associated with the racial composition of areas, that homes in black neighborhoods are worth less, and that they accumulate less value over time.

Project Leads: William Darity Jr.

Project Manager: Omer Ali

Click here to view the team's final project slides

 

Watch the team's final presentation (on Zoom) here:

Network Visualization of Foot Traffic Patterns

In light of Duke’s reopening amidst the COVID-19 pandemic, this project aims to track the movement of foot traffic in and around Bryan Center by analyzing Wifi log data from all users connected to wireless networks in the center during February 2020. Our team employed Markov Chains, Kernel Density Estimations, and data analysis and visualization tools such as Python and Tableau to create a map of Wifi access points in Bryan Center and a heatmap that visualizes congestion in different floor areas across time. Our goal is to provide Duke OIT and Student Affairs with valuable information on highly congested areas and frequented paths, directing social distancing measures and suggesting alternative paths that can reduce transmission-risk this coming academic year.

Project Leads: John Haws, Mary Thompson, Eric Hope, Sean Dilda

Project Manager: Hunter Klein

Click here to view the project team's poster

Watch the team's final project presentation (on Zoom) here:

 

Mental Health and the Justice System in Durham County

This summer, our objective was to take data provided by the Durham County Detention Facility (DCDF), Duke Health, and Lincoln Community Health Center and analyze trends across the local justice system and these health care institutions, specifically in regards to individuals with mental illness. We analyzed the experience of individuals who were incarcerated by looking at their demographic characteristics, emergency department usage, and criminal justice encounters. Using these initial findings, we hope to better understand the relationship between health care utilization and rates of recidivism in Durham County during the school year through a Bass Connections Team.

Project Leads: Nicole Schramm-Sapyta, Maria Tackett

Project Manager: Ruth Wygle

 

Click here to view the team's final project summary

 

Watch the team's final presentation (on Zoom) here:

 

 

Latinxs and the Right

The visibility of hate groups such as the Alt-Right became mainstream into contemporary political culture during the Unite the Right Rally in Charlottesville, VA in 2017. This project aims to explore methods to quantify the presence of Latinxs within the Alt-Right, particularly in how they racialize themselves in a space that often spews hate towards Mexicans and other marginalized groups from Latin America. Using data from multiple sources (such as Twitter, Stormfront, and Breitbart), we developed a corpus of tweets, subthreads, and articles, and analyzed this data using basic natural language processing (NLP) techniques.

Project Lead: Cecilia Márquez

Project Manager: Susan Jacobs

 

Click here to view the team's project summary slides

 

Watch the team's final presentation (on Zoom) here:

For love of greed: tracing the early history of consumer culture

We apply word embedding models to corpora from the start of the Early Modern period, when the market economy began to dramatically expand in England. Word embedding models use neural networks to map vectors to words so that semantic relationships are preserved within the vectors’ geometry. Such models have been successful in understanding cultural trends and stereotypes in large corpora of texts, but these techniques are infrequently used on texts dating much farther back than the 19th century. Using newly developed methods for analyzing word embeddings, we track the development of the meanings of words related to consumerism, including their relationships with gender over time.

 

Project Leads: Astrid Giugni, Jessica Hines

Project Manager: Chris Huebner

 

Click here to view the team's final poster

 

Watch the team's final presentation (on Zoom) here:

Protecting American Investors? Financial Advice from before the New Deal to the Birth of the Internet

The Protecting American Investors project investigates the evolving structure and content of financial advice from the early 20th century to the birth of the Internet. By converting and cleaning thousands of investment advice columns from historical newspapers and magazines, we assembled a large corpus to address our research questions. Through text analysis methods like topic modeling, we have seen how the business cycle affects the nature of advice, the speed in which different financial innovations were integrated, and how advice differs among various targeted social groups.

Project Lead: Ed Balleisen

 

Click here to view the team's final project summary slides

 

Watch the team's final presentation (on Zoom) here:

 

 

On Being a Blue Devil: the changing profile of the Duke student body

Duke’s enrollment data over the past 50 years speaks volumes about the evolution of the University. Using physical scans courtesy of the Duke University Archives, we manipulate decades of demographic and geospatial data across all Duke schools to create an interactive application. Giving users control over how they want to dive into the data, we hope to both illuminate questions and inspire further research into the composition of Duke’s student body--highlighting the history, origins, and people that have helped make it what it is today.

Project Leads: Don Taylor, Valerie Gillispie

Project Manager: Anna Holleman

 

Watch the project team's final presentation (on Zoom) here:

 

The team discusses insights they have learned through looking at Duke's enrollment data since the founding of Duke:

 

Taking electrification on the road: Exploring the impact of the Electric Farm Equipment roadshow

Between 1935 and 1945, rural electricity access shot up from roughly 10% to 90%. During this time, the Rural Electrification Administration funded an Electric Farm Equipment (EFE) Roadshow as part of its mission to expand electricity access and demand. Digitizing massive amounts of archival data, our team has sought to quantify the effect of the EFE Roadshow on the larger trend of growing residential electricity consumption in rural U.S. towns from 1938 to 1945. We hope that understanding this crucial chapter in our own history will help inform present-day electrification efforts in the developing world.

 

Project Leads: Victoria Plutshack, Jonathon Free, Robert Fetter

 

View the team's final project summary here

 

Watch the team's final presentation (on Zoom) here:

Disease Emergence and Richness in Primates

The Disease Emergence and Richness in Primates team uses existing databases to quantify parasite richness across primates and to identify ecological predictors of parasitism. By integrating phylogenetic generalized least squares regression and network based approaches, the team ultimately aims to predict missing interactions between primates and parasites, which, combined with exploring ecological predictors, will provide better capabilities for identifying emerging infectious diseases in humans.

 

Project Leads: Jim Moody, Charles Nunn

Project Manager: Marie Claire Chelini

 

Click here to view the team's final project summary

ABOUT-US – A BOundary Update Tool for Utility Services

We all need water to survive but how many of us really know where our water comes from? Team 3 has created a functioning website from scratch to give consumers more easily accessible information about who provides their water and how supply looks in relation to the past 30 years.

Project Leads: Megan Mullin, Lauren Patterson

 

Watch the team's final presentation (on Zoom) here:

Project Manager: Kyle Onda

When Black Stories Go Global: Analyzing the Translation of African-American Literature and Film

Led by Dr. Eva Wheeler, this project considers how racial language in African American literature and film is rendered for international audiences and traces the spread of these translations. To address the study’s primary questions, the team analyzed a preliminary dataset and explored the relationship between translation strategy and different categories of racial language. The team also conducted a macro-level analysis of the linguistic, temporal, and geographic spread of African American stories using the IMDB and WorldCat databases. We have found a large amount of variation in how African American stories are rendered, which can in part be explained through a social scientific lens.

 

Project Lead: Eva Wheeler

 

Project Manager: Bernard Coles

 

Click here to view the team's project poster

 

Watch the team's final presentation (in Zoom) here:

Female Reproduction: Sexual Swellings, Hormones, and Mating in Wild Baboons

This project allowed students in BIOL 268D (Mechanisms of Animal Behavior) to explore the relationship between estrogen, female sexual swellings, and male mating success in wild baboons using data from the Amboseli Baboon Research Project. Students learned how to use the popular R packages dplyr and ggplot2 to calculate descriptive statistics about the dataset and perform data visualization to understand and explore patterns in animal mating behavior and sexual signals.

Visualizing Twitter: Public Discourse and the 2018 Florida Midterm Election Recounts

This two-week teaching module in an introductory-level undergraduate course invites students to explore the power of Twitter in shaping public discourse. The project supplements the close-reading methods that are central to the humanities with large-scale social media analysis. This exercise challenges students to consider how applying visualization techniques to a dataset too vast for manual apprehension might enable them to identify for granular inspection smaller subsets of data and individual tweets—as well as to determine what factors do not lend themselves to close-reading at all. Employing an original dataset of almost one million tweets focused on the contested 2018 Florida midterm elections, students develop skills in using visualization software, generating research questions, and creating novel visualizations to answer those questions. They then evaluate and compare the affordances of large-scale data analytics with investigation of individual tweets, and draw on their findings to debate the role of social media in shaping public conversations surrounding major national events. This project was developed as a collaboration among the English Department (Emma Davenport and Astrid Giugni), Math Department (Hubert Bray), Duke University Library (Eric Monson), and Trinity Technology Services (Brian Norberg).

Queens of Antiquity

Understanding how to generate, analyze, and work with datasets in the humanities is often a difficult task without learning how to code or program. In humanities centered courses, we often privilege close reading or qualitative analysis over other methods of knowing, but by learning some new quantitative techniques we better prepare the students to tackle new forms of reading. This class will work with the data from the HathiTrust to develop ideas for thinking about how large groups and different discourse communities thought of queens of antiquity like Cleopatra and Dido.

Please refer to https://sites.duke.edu/queensofantiquity/ for more information.

Basketball analytics pipeline---from raw video to dynamic visualization

Team A: Video data extraction

Alexander Bendeck (Computer Science, Statistics) and Niyaz Nurbhasha (Economics) spent ten weeks building tools to extract player and ball movement in basketball games. Using freely available broadcast-angle video footage which required much cleaning and pre-processing, the team used OpenPose software and employed neural network methodologies. Their pipeline fed into the predictive models of Team C.

Click here to read the Executive Summary

 

Team B: Modeling basketball data: offense

Anshul Shah (Computer Science, Statistics), Jack Lichtenstein (Statistics), and Will Schmidt (Mechanical Engineering) spent ten weeks building tools to analyze offensive play in basketball. Using 2014-5 Duke Men’s Basketball player-tracking data provided by SportVU, the team constructed statistical models that explored the relationship between different metrics of offensive productivity, and also used computational geometry methods to analyze the off-ball “gravity” of an offensive player.

Click here to read the Executive Summary

 

Team C: Modeling basketball data: defense

Lukengu Tshiteya (Statistics), Wenge Xie (ECE), and Joe Zuo (Computer Science, Statistics) spent ten weeks building tools to predict player movement in basketball games. Using SportVU data, including some pre-processed by Team A, the team built predictive RNN models that distinguish between 6 typical movement types, and created interactive visualizations of their findings in R Shiny.

Click here to read the Executive Summary

 

Team D: Visualizing basketball data

Shixing Cao (ECE) and Jackson Hubbard (Computer Science, Statistics) spent ten weeks building visualizations to help analyze basketball games. Using player tracking data from Duke basketball games, the team created visualizations of gameflow, networks of points and assists, and integrated all of their tools into an R Shiny app.

Click here to read the Executive Summary

 

Faculty Leads: Alexander Volfovsky, James Moody, Katherine Heller

Project Managers: Fan Bu, Heather Matthews, Harsh Parikh, Joe Zuo

Network Visualization of IoT devices

Bernice Meja (Philosophy, Physics), Jessica Yang (Computer Science, ECE), and Tracey Chen (Computer Science, Mechanical Engineering) spent ten weeks building methods for Duke’s Office of Information Technology (OIT) to better understand information arising from “smart” (IoT) devices on campus. Working with data provided by an IoT testbed set up by OIT professionals, the team used a mixture of supervised and unsupervised machine-learning techniques and built a prototype device classifier.

 

Click here ot read the Executive Summary

 

Project Lead: Will Brockselsby

Security Threat Intelligence Analytics

Interested in understanding the types of attacks targeting Duke and other universities?  Led by OIT and the IT Security Office, students will learn to analyze threat intelligence data to identify trends and patterns of attacks.  Duke blocks an average of 1.5 billion malicious connection attempts/day and is working with other universities to share the attack data.  One untapped area is research into the types of attacks and learning how universities are targeted.  Students will collaborate alongside the security and IT professionals in analyzing the data and with the intent to discern patterns.

Project Lead: Jesse Bowling

Project Manager: Susan Jacobs

Breaking the Bundle: Analyzing Duke’s Journal Subscriptions

Cathy Lee (Statistics) and Jennifer Zheng (Math, Emory University) spent ten weeks building tools to help Duke University Libraries better understand its journal purchasing practice. Using a combination of web-scraping and data-merging algorithms, the team created a dashboard to help library strategists visualize and optimize journal selection.

 

Click here to read the Executive Summary

 

 

 

 

Faculty Leads: Angela Zoss, Jeff Kosokoff

Project Manager: Chi Liu

Neuroscience in the Courtroom

 Micalyn Struble (Computer Science, Public Policy), Xiaoqiao Xing (Economics), and Eric Zhang (Math) spent ten weeks exploring the use of neuroscience as evidence in criminal trials. Working with a large set of case files downloaded from WestLaw, the team used natural language processing to build a predictive model that has the potential to automate the process of locating relevant-to-neuroscience cases from databases.

 

Click here to read the Executive Summary

 

Faculty Lead: Nita Farahany

Project Manager: William Krenzer

Remembering the Middle Passage

The Middle Passage, the route by which most enslaved persons were brought across the Atlantic to North America, is a critical locus of modern history—yet it has been notoriously difficult to document or memorialize. The ultimate aim of this project is to employ the resources of digital mapping technologies as well as the humanistic methods of history, literature, philosophy, and other disciplines to envision how best to memorialize the enslaved persons who lost their lives between their homelands and North America. To do this, the students combined previously-disparate data and archival sources to discover where on their journeys enslaved persons died. Because of the nature of data itself and the history it represents, the team engaged in on-going conversations about various ways of visualizing its findings, and continuously evaluated the ethics of the data’s provenance and their own methodologies and conclusions. A central goal for the students was to discover what contribution digital data analysis methods could make to the project of remembering itself.

 

The group worked with two datasets: the Trans-Atlantic Slave Trade Database (www.slavevoyages.org), an SPSS-formatted database currently run out of Emory University, containing data on 36,002 individual slaving expeditions between 1514 and 1866; and the Climatological Database for the World’s Oceans 1750-1850 (CLIWOC) (www.kaggle.com/cwiloc/climate-data-from-ocean-ships), a dataset composed of digitized records from the daily logbooks of ocean vessels, originally funded by the European Union in 2001 for purposes of tracking historical climate change. This second dataset includes 280,280 observational records of daily ship locations, climate data, and other associated information. The team employed archival materials to confirm (and disconfirm) overlaps between the two datasets: the students identified 316 ships bearing the same name across the datasets, of which they confirmed 35 matching slaving voyages.

 

The students had two central objectives: first, to locate where and why enslaved Africans died along the Middle Passage, and, second, to analyze patterns in the mortality rates. The group found significant patterns in the mortality data in both spatial and temporal terms (full results can be found here). At the same time, the team also examined the ethics of creating visualizations based on data that were recorded by the perpetrators of the slave trade—opening up space for further developments of this project that would include more detailed archival and theoretical work.

 

Click here to read the Executive Summary

 

Image credit:

J.M.W. Turner, Slave Ship, 1840, Museum of Fine Arts, Boston (public domain)

Faculty Lead: Charlotte Sussman

Project Manager: Emma Davenport

Durham Evictions

Ellis Ackerman (Math, NCSU), Rodrigo Araujo (Computer Science), and Samantha Miezio (Public Policy) spent ten weeks building tools to help understand the scope, cause, and effects of evictions in Durham County. Using evictions data recorded by the Durham County Sheriff’s Department and demographic data from the American Community Survey, the team investigated relationships between rent and evictions, created cost-benefit models for eviction diversion efforts, and built interactive visualizations of eviction trends. They had the opportunity to consult with analytics professionals from DataWorks NC.

Project Leads: Tim Stallmann, John Killeen, Peter Gilbert

Project Manager: Libby McClure

 

Human Rights in the Postwar World

The aim of this project was to explore how U.S. mass media—particularly newspapers—enlists text and imagery to portray human rights, genocide, and crimes against humanity from World War II until the present. From the Holocaust to Cambodia, from Rwanda to Myanmar, such representation has political consequences. Coined by Raphael Lemkin, a Polish lawyer who fled Hitler’s antisemitism, the term “genocide” was first introduced to the American public in a Washington Post op-ed in 1944. Since its legal codification by the United Nations Convention for the Prevention of Genocide in 1948, the term has circulated, been debated, used to describes events that pre-date it (such as the displacement and genocide of Native People in the Americas), and been shaped by numerous forces—especially the words and images published in newspapers. Alongside the definition of “genocide,” other key concepts, specifically “crimes against humanity,” have attempted to label, and thus name the story, of targeted mass violence. Conversely, the concept of “human rights,” enshrined in the 1948 UN Declaration, seeks to name a presence of rights instead of their absence.

 

During the summer, the team focused their work on evaluating the language used in Western media to represent instances of genocide and how such language varied based on the location and time period of the conflict. In particular, the team’s efforts centered on Rwanda and Bosnia as important case studies, affording them the chance to compare nearly simultaneous reporting on two well-known genocides. The language used by reporters in these two cases showed distinct polarizations of terminology (for instance, while “slaughter” was much more common than “murder” in discussions of the Rwanda genocide, the inverse was true for Bosnia).

 

Click here to read the Executive Summary

 

Faculty Leads: Nora Nunn, Astrid Giugni

U.S. Ambivalence About Making Profits

How Much Profit is Too Much Profit?

Chris Esposito (Economics), Ruoyu Wu (Computer Science), and Sean Yoon (Masters, Decision Sciences) spent ten weeks building tools to investigate the historical trends of price gouging and excess profits taxes in the United States of America from 1900 to the present. The team used a variety of text-mining methods to create a large database of historical documents, analyzed historical patterns of word use, and created an interactive R Shiny app to display their data and analyses.

Click here to read the Executive Summary

 

(cartoon from The Masses July 1916)

Faculty Lead: Sarah Deutsch

Project Manager: Evan Donahue

Optimizing Risk Assessment for Duke University Student Athlete Injury Prevention

Maria Henriquez (Computer Science, Statistics) and Jacob Sumner (Biology) spent ten weeks building tools to help the Michael W. Krzyzewski Human Performance Lab best utilize its data from Duke University student athletes. The team worked with a large collection of athlete strength, balance, and flexibility measurements collected by the lab. They improved the K Lab’s data pipeline, created a predictive model for injury risk, and developed interactive web-based individualized injury risk reports.

Click here to read the Executive Summary

Faculty Lead: Dr. Tim Sell
Project Manager: Brinnae Bent

 

 

Recidivism in the Durham County Jail

Aidan Fitzsimmons (Public Policy, Mathematics, Electrical & Computer Engineering), Joe Choo (Mathematics, Economics) and Brooke Scheinberg (Mathematics) spent ten weeks partnering with the Durham Crisis Intervention Team, the Criminal Justice Resource Center, and the Stepping Up Initiative. Utilizing booking data of 57,346 individuals provided by the Durham County Jail, this team was able to create visualizations and predictive models that illustrate patterns of recidivism, with a focus on the subset of the population with serious mental illness (SMI). These results could assist current efforts in diverting people with SMI from the criminal justice system and into care.

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Faculty Lead: Nicole Schramm-Sapyta, Michele Easter

Project Manager: Ruth Wygle

Invisible Adaptations: From Hamlet to the Avengers

The students in this project worked on a pervasive question in literary, film, and copyright studies: how do we know when a new work of fiction borrows from an older one? Many times, works are appropriated, rather than straightforwardly adapted, which makes it difficult for human readers to trace. As we continue to remake and repurpose previous texts into new forms that combine hundreds of references to other works (such as Ready Player One), it becomes increasingly laborious to track all the intertextual elements of a single text. While some borrowings are easy to spot, as in the case of Marvel films that are straightforward adaptations of comic book storylines and aesthetics, others are more subtle, as when Disney reinterpreted Hamlet and African oral traditions to create The Lion King. Thousands of new stories are created each day, but how do we know if we are borrowing or appropriating a previous text? Are there works that have adapted previous ones that we have yet to identify?

 

The students worked with data from over 16.7 million books from Hathitrust, with critical analysis in scholarly articles accessible through JSTOR, and with the topic categories in Wikipedia. The group used Latent Dirichlet Allocation (LDA), a generative model that assumes that all documents are a mixture of topics, to represent key themes and topics as a distribution over words. The students developed a flexible and graduated heuristic for identifying a work as an adaptation; the more pre-selected categories a work fit under, the more likely it was to be marked as an adaptation by their model. Over the summer, the students came to appreciate that all digital humanistic methodologies are contestable and dependent on traditional critical work.

 

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Faculty Lead: Grant Glass

American Predatory Lending & the Global Financial Crisis

Jett Hollister (Mechanical Engineering) and Lexx Pino (Computer Science, Math) joined Economics majors Shengxi Hao and Cameron Polo in a ten week study of the late 2000s housing bubble. The team scraped, merged, and analyzed a variety of datasets to investigate different proposed causes of the bubble. They also created interactive visualizations of their data which will eventually appear on a website for public consumption.

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Faculty Lead: Lee Reiners

Project Manager: Kate Coulter

Speech Emotion Analysis

Andre Wang (Math, Statistics), Michael Xue (Computer Science, ECE), and Ryan Culhane (Computer Science) spent ten weeks exploring the role played by emotion in speech-focused machine-learning. The team used a variety of techniques to build emotion recognition pipelines, and incorporated emotion into generated speech during text-to-speech synthesis.

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Faculty Leads: Vahid Tarokh, Jie Ding

Project Manager: Enmao Diao

Networks, Higher Education, and Social Imaginaries

This Data Expedition introduces students to network tools and approaches and invites students to consider the relationship(s) between social networks and social imaginaries. Using foundation-funding data that was collected from the The Foundation Directory Online, the Data Expedition enables students to visualize and explore the relationship between networks, social imaginaries, and funding for higher education. The Data Expedition is based on two sets of data. The first set list the grants received by Duke University in 2016 from five foundations: The Bill and Melinda Gates Foundation, Fidelity Charitable Gift Fund, Silicon Valley Community Foundation, The Community Foundation of Western North Carolina, and The Robert Wood Johnson Foundation. The second set lists the names of board members from Duke University and each of these five foundations along with the degree granting institution for their undergraduate education. For the sake of this exercise, the degree granting institutions data was fabricated from a randomized list of the top twenty-five undergraduate institutions.

Exploring Poverty with Principal Component Analysis

This Data Expedition seeks to introduce students to statistical analysis in the field of international development. Students construct a index of wealth/poverty based on asset holdings using four datasets collected under the umbrella of the Living Standards Measurement Survey project at the World Bank. We selected countries to represent different continents with comparable and recent survey data: Bulgaria (2007), Tajikistan (2009), Tanzania (2010-2011), and Panama (2008).

First, we construct an index of wealth based on household assets in the different countries using Principle Components Analysis. Once a poverty index is constructed, students seek to understand what the main drivers of wealth/poverty are in different countries. We include variables for health, education, age, relationship to the household head, and sex. Students then use regression analysis to identify the main drivers of poverty in different countries.

Exploring Patent Networks Using U.S. Patent and Trademark Office Patent Records

This data expedition explores the local (ego) patent citation networks of three hybrid vehicle-related patents. The concept of patent citations and technological development is a core theme in innovation and entrepreneurship, and the purpose of these network explorations is to both quantitatively and visually assess how innovations are connected and what these connections mean for the focal innovations and the technologies that draw on those patents in the future. The expedition was incorporated as part of the Sociology of Entrepreneurship class, where students are thinking about the emergence and diffusion of innovations.

To Catch a Thief (with Data!)

Our aim was to introduce students to the wealth of possibilities that human genotyping and sequencing hold by illustrating firsthand the power of these datasets to identify genetic relatives, using the story of the Golden State Killer’s capture with public genetic databases.

Answering Biological Questions Using Circular Data and Analysis in R

This Data Expedition introduced hypothesis-driven data analysis in R and the concept of circular data, while providing some tools for importing it and analyzing it in R.

Co-Curricular Technology Pathways e-advisor

Alec Ashforth (Economics/Math), Brooke Keene (Electrical & Computer Engineering), Vincent Liu (Electrical & Computer Engineering), and Dezmanique Martin (Computer Science) spent ten weeks helping Duke’s Office of Information Technology explore the development of an “e-advisor” app that recommends co-curricular opportunities to students based on a variety of factors. The team used collaborative and content-based filtering to create a recommender-system prototype in R Shiny.

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Duke Wireless Data

Statistical Science majors Eidan Jacob and Justina Zou joined forces with math major Mason Simon built interactive tools that analyze and visualize the trajectories taken by wireless devices as they move across Duke’s campus and connect to its wireless network. They used de-identified data provided by Duke’s Office of Information Technology, and worked closely with professionals from that office.

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Data and Technology for Fact-Checking

Lucas Fagan (Computer Science/Public Policy), Caroline Wang (Computer Science/Math), and Ethan Holland (Statistics/Computer Science) spent ten weeks understanding how data science can contribute to fact-checking methodology. Training on audio data from major news stations, they adapted OpenAI methods to develop a pipeline that moves from audio data to an interface that enables users to search for claims related to other claims that had been previously investigated by fact-checking websites.

This project will continue into the academic year via Bass Connections.

Click here to read the Executive Summary.

Gerrymandering and the Extent of Democracy in America

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.

Social Determinants of Health

Kimberly Calero (Public Policy/Biology/Chemistry), Alexandra Diaz (Biology/Linguistics), and Cary Shindell (Environmental Engineering) spent ten weeks analyzing and visualizing data about disparities in Social Determinants of Health. Working with data provided by the MURDOCK Study, the American Community Survey, and the Google Places API, the team built a dataset and visualization tool that will assist the MURDOCK research team in exploring health outcomes in Cabarrus County, NC.

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Complex Decisions, Real Numbers: Medical Decision-Making

Samantha Garland (Computer Science), Grant Kim (Computer Science, Electrical & Computer Engineering), and Preethi Seshadri (Data Science) spent ten weeks exploring factors that influence patient choices when faced with intermediate-stage prostate cancer diagnoses. They used topic modeling in an analysis of a large collection of clinical appointment transcripts.

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Women's Spaces

Nathan Liang (Psychology, Statistics), Sandra Luksic (Philosophy, Political Science),and Alexis Malone (Statistics) began their 10-week project as an open-ended exploration how women are depicted both physically and figuratively in women's magazines, seeking to consider what role magazines play in the imagined and real lives of women.

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Visualizing the Lives of Orphaned and Separated Children

Jennie Wang (Economics/Computer Science) and Blen Biru (Biology/French) spent ten weeks building visualizations of various aspects of the lives of orphaned and separated children at six separate sites in Africa and Asia. The team created R Shiny interactive visualizations of data provided by the Positive Outcomes for Orphans study (POFO).

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How do we build and grow a PTA?

Aaron Crouse (Divinity), Mariah Jones (Sociology), Peyton Schafer (Statistics), and Nicholas Simmons (English/Education) spent ten weeks consulting with leadership from the Parents Teacher Association at Glenn Elementary School in Durham. The team set up infrastructure for data collection and visualization that will aid the PTA in forming future strategy.

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Pirating Texts

In tracing the publication history, geographical spread, and content of “pirated” copies of Daniel Defoe’s Robinson Crusoe, Gabriel Guedes (Math, Global Cultural Studies), Lucian Li (Computer Science, History), and Orgil Batzaya (Math, Computer Science) explored the complications of looking at a data set that saw drastic changes over the last three centuries in terms of spelling and grammar, which offered new challenges to data cleanup. By asking questions of the effectiveness of “distant reading” techniques for comparing thousands of different editions of Robinson Crusoe, the students learned how to think about the appropriateness of myriad computational methods like doc2vec and topic modeling. Through these methods, the students started to ask, at what point does one start seeing patterns that were invisible at a human scale of reading (reading one book at a time)? While the project did not definitively answer these questions, it did provide paths for further inquiry.

The team published their results at: https://orgilbatzaya.github.io/pirating-texts-site/

Click here for the Executive Summary

Poverty in Writing & Images

Ashley Murray (Chemistry/Math), Brian Glucksman (Global Cultural Studies), and Michelle Gao (Statistics/Economics) spent 10 weeks analyzing how meaning and use of the work “poverty” changed in presidential documents from the 1930s to the present. The students found that American presidential rhetoric about poverty has shifted in measurable ways over time. Presidential rhetoric, however, doesn’t necessarily affect policy change. As Michelle Gao explained, “The statistical methods we used provided another more quantitative way of analyzing the text. The database had around 130,000 documents, which is pretty impossible to read one by one and get all the poverty related documents by brute force. As a result, web-scraping and word filtering provided a more efficient and systematic way of extracting all the valuable information while minimizing human errors.” Through techniques such as linear regression, machine learning, and image analysis, the team effectively analyzed large swaths of textual and visual data. This approach allowed them to zero in on significant documents for closer and more in-depth analysis, paying particular attention to documents by presidents such as Franklin Delano Roosevelt or Lyndon B. Johnson, both leaders in what LBJ famously called “The War on Poverty.”

Click Here for the Executive Summary

Analytical Exploration for Duke Development

Natalie Bui (Math/Economics), David Cheng (Electrical & Computer Engineering), and Cathy Lee (Statistics) spent ten weeks helping the Prospect Management and Analytics office of Duke Development understand how a variety of analytic techniques might enhance their workflow. The team used topic modeling and named entity recognition to develop a pipeline that clusters potential prospects into useful categories.

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Mental Health Interventions by the Durham Police (Year 2)

Tatanya Bidopia (Psychology, Global Health), Matthew Rose (Computer Science), Joyce Yoo (Public Policy/Psychology) spent ten weeks doing a data-driven investigation of the relationship between mental health training of law enforcement officers and key outcomes such as incarceration, recidivism, and referrals for treatment. They worked closely with the Crisis Intervention Team, and they used jail data provided by the Sheriff’s Office of Durham County.

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Transgender Discrimination Survey

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.

Data-driven Parking

ECE majors Mitchell Parekh and Yehan (Morton) Mo, along with IIT student Nikhil Tank, spent ten weeks understanding parking behavior at Duke. They worked closely with the Parking and Transportation Office, as well as with Vice President for Administration Kyle Cavanaugh.

Workforce Incentives

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.

Fruit Fly Morphogenesis

BME major Neel Prabhu, along with CompSci and ECE majors Virginia Cheng and Cheng Lu, spent ten weeks studying how cells from embryos of the common fruit fly move and change in shape during development. They worked with Cell-Sheet-Tracker (CST), an algorithm develped by former Data+ student Roger Zou and faculty lead Carlo Tomasi. This algorithm uses computer vision to model and track a dynamic network of cells using a deformable graph.

Black Queen Hypothesis

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.

Learning to Search More Deeply

Weiyao Wang (Math) and Jennifer Du , along with NCCU Physics majors Jarrett Weathersby and Samuel Watson, spent ten weeks learning about how search engines often provide results which are not representative in terms of race and/or gender. Working closely with entrepreneur Winston Henderson, their goal was to understand how to frame this problem via statistical and machine-learning methodology, as well as to explore potential solutions.

Data-driven Development

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.

Smart(er) Routing at Theme Parks

Runliang Li (Math), Qiyuan Pan (Computer Science), and Lei Qian (Masters in Statistics and Economic Modelling) spent ten weeks investigating discrepancies between posted wait times and actual wait times for rides at Disney World. They worked with data provided by TouringPlans.

Mental Health Interventions by Durham Police

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.

Alumni Gifts and Data Analysis

Building off the work of a 2016 Data+ team, Yu Chen (Economics), Peter Hase (Statistics), and Ziwei Zhao (Mathematics), spent ten weeks working closely with analytical leadership at Duke's Office of University Development. The project goal was to identify distinguishing characteristics of major alumni donors and to model their lifetime giving behavior.

Ghost Bikes

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.

Comparing the Exploration of Academic Majors at Duke

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.

Smart-phone-assisted digital rejuvenation of medieval paintings

Liuyi Zhu (Computer Science, Math), Gilad Amitai (Masters, Statistics), Raphael Kim (Computer Science, Mechanical Engineering), and Andreas Badea (East Chapel Hill High School) spent ten weeks streamlining and automating the process of electronically rejuvenating medieval artwork. They used a 14th-century altarpiece by Francescussio Ghissi as a working example.

Understanding Duke Research Based on Large-Scale Faculty Publication Records

John Benhart (CompSci, Math) and Esko Brummel (Masters in Bioethics and Science Policy) spent ten weeks analyzing current and potential scholarly collaborations within the community of Duke faculty. They worked closely with the leadership of the Scholars@Duke database.

Online Financial Behavior and the Internet of Things

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.

Marriage and Statistics through Space and Time

Over ten weeks, Mathematics/Economics majors Khuong (Lucas) Do and Jason Law joined forces with Analytical Political Economy Masters student Feixiao Chen to analyze the spati-temporal distribution of birth addresses in North Carolina. The goal of the project was to understand how/whether the distributions of different demographic categories (white/black, married/unmarried, etc.) differed, and how these differences connected to a variety of socioeconomic indicators.

Analytics for Faculty Success

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.

Baseball Analytics with Statcast

Graduate Student: Jacob Coleman, 3rd year Ph.D. student in Statistical Science

Faculty Instructor: Colin Rundel

Class: STA 112, Data Science

Team Science

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.

Geometry and Topology for Data

Computer Science majors Erin Taylor and Ian Frankenburg, along with Math major Eric Peshkin, spent ten weeks understanding how geometry and topology, in tandem with statistics and machine-learning, can aid in quantifying anomalous behavior in cyber-networks. The team was sponsored by Geometric Data Anaytics, Inc., and used real anonymized Netflow data provided by Duke's Information Technology Security Office.

Machine Society Interfaces

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.

Dig@IT: Virtual Reality in Archaeology

A virtual reality system to recreate the archaeological experience using data and 3D models from the neolithic site of Çatalhöyük, in Anatolia, Turkey. 

Archives Alive: The North Carolina Jukebox Project

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. 

Interactive environmental data applications

Molly Rosenstein, an Earth and Ocean Sciences major and Tess Harper, an Environmental Science and Spanish major spent ten weeks developing interactive data applications for use in Environmental Science 101, taught by Rebecca Vidra.

Compressed Nonnegative Matrix Factorization Is Fast and Accurate

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.

Constructing Challenges from Duke MOOC data

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.

microRNA Host Response to Infection

Kelsey Sumner, EvAnth and Global Health major and Christopher Hong, CompSci/ECE major, spent ten weeks analyzing high-dimensional microRNA data taken from patients with viral and/or bacterial conditions. They worked closely with the medical faculty and practitioners who generated the data.

Risky Decision-Making

Ethan Levine, Annie Tang, and Brandon Ho spent ten weeks investigating whether personality traits can be used to predict how people make risky decisions. They used a large dataset collected by the lab of Prof. Scott Huettel, and were mentored by graduate students Emma Wu Dowd and Jonathan Winkle.

Quantifying the Science-Humanities Gap

Spenser Easterbrook, a Philosophy and Math double major, joined Biology majors Aharon Walker and Nicholas Branson in a ten-week exploration of the connections between journal publications from the humanities and the sciences. They were guided by Rick Gawne and Jameson Clarke, graduate students from Philosophy and Biology.