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.

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

In this project, we are interested in creating a cohesive data pipeline for generating, modeling and visualizing basketball data. In particular, we are interested in understanding 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. We will have four separate teams as part of this project working on interrelated but separate goals:

Team 1: Video data extraction

This team will explore different video data extraction techniques with the goal of identifying player locations, ball location and events at any given time during a basketball game. The software developed as part of this project will be able to generate a usable dataset of time-stamped basketball plays that can be used to model the game of basketball.

Teams 2 & 3: Modeling basketball data: offense and defense

The two teams will explore different models for the game of basketball. The first team will concentrate on modeling offensive plays and try to answer questions such as: How does the ball advance? What leads to successful plays? The second team will concentrate on defensive plays: What is an optimal strategy for minimizing opponent scoring opportunities? How should we evaluate defensive plays?

Team 4: Visualizing basketball data

This team will work on dynamic and static visualization of elements of a basketball game. The goal of the visualization is to capture information about how players and the ball move around the court. They will develop tools to represent average trajectories be in these settings that can also capture uncertainty about this information.

Faculty Leads: Alexander Volfovsky, James Moody, Katherine Heller

Project Managers: Fan Bu, Heather Matthews, Harsh Parikh, TBD

Network Visualization of IoT devices

A team of students, in conjunction with Duke’s Office of Information Technology, will use of Duke’s network traffic data to perform IoT device behavioral fingerprinting that can be employed to identify device types. The data will be used to analyze trends and risks, develop security best practices, and build machine learning models that can be used to detect similar device types. 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.

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

A team of students partnering with Duke University Libraries will explore the complicated decision space of electronic journal licensing. Electronic resources like journal articles are a major service provided by academic libraries, but the choice of what journal subscriptions to purchase can be costly and time consuming, and journal distribution companies like Elsevier manipulate their journal bundles to maximize their own profits. This team will build a model for journal purchasing by combining several years of journal usage data (including view, downloads, authorship, citations, and impact) with journal cost data. The team will work on software to improve the data cleaning and analysis process and will create visualizations and dashboards to assist the library in its decision-making efforts. Because many libraries have the same concerns about journal bundles and use the same kinds of data to make these decisions, this project may have far-reaching impacts among academic libraries.

Faculty Leads: Angela Zoss, Jeff Kosokoff

Project Manager: Chi Liu

Neuroscience in the Courtroom

Neuroscience evidence (e.g., brain scans, mental-illness diagnosis, etc.) 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 to 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. Additionally, with the use of our comprehensive 10-year judicial opinion data set (2005-2015), the team will collaborate on creating a computer algorithm to assist in locating and coding online judicial opinions to build upon our comprehensive list of opinions. This tool will provide a strong foundation in the work of understanding neuroscience’s role within a criminal court setting.

Faculty Lead: Nita Farahany

Project Manager: William Krenzer

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. While scholars have studied the overall mortality of the slave trade, little is known about where these deaths occurred. New mapping technologies can begin to supply this data. 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. This project will employ the resources of digital technologies as well as the humanistic methods of history, literature, philosophy, and other disciplines. The project welcomes students from a broad range of disciplines: computer science; mathematics; English and literature; history; African and African American studies; philosophy; art history; visual and media studies; geography; climatology; and ocean science.

 

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

Students will collaborate with staff at DataWorks NC and the Eviction Diversion Program to explore and develop means of using evictions data to drive meaningful policy change that help Durham residents stay in their homes. Students will clean and assess the quality of evictions data, look for seasonal and geographic variation in eviction rates, analyze the relationship between evictions, rents, wages and other economic indicators, develop metrics for the real financial cost of evictions, and build static visualizations or a data dashboard to communicate their results. This project will help housing advocates in Durham assess the impact of their current work, and understand which future interventions will be most impactful.

Project Leads: Tim Stallmann, John Killeen, Peter Gilbert

Project Manager: Libby McClure

Human Rights in the Postwar World

The American public first encountered the term “genocide” in a Washington Post op-ed published in 1944; since then, the word’s meaning has been circulated, debated, and shaped by numerous forces, especially by words and images in newspapers. With the support of Dr. Priscilla Wald (English), a team of students led by Nora Nunn (English graduate student) and Astrid Giugni (English and ISS) will analyze how U.S. mass media—particularly newspapers—enlist text and imagery such as press photographs to portray genocide, human rights, and crimes against humanity from World War II to the present. From the Holocaust to Cambodia, from Rwanda to Myanmar, such representation has political consequences. If time allows, students will also study the representation of collective violence in Hollywood film, querying the relationship between human rights and genre. The implications of these findings could inform future coverage of human rights-related issues at home and abroad.

Faculty Leads: Nora Nunn, Astrid Giugni

U.S. Ambivalence About Making Profits

How Much Profit is Too Much Profit?

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. As of 2018 numerous states have price gouging laws. Why? How did they define what was excessive? How did this critique of profit-making become mainstream without endangering capitalism? By searching extant newspaper and Congressional databases for the frequency and context of particular words and phrases, the project will begin to uncover the logic and language and the partisanship or lack of it used to critique profits at three moments in U.S. History that resulted in government action to limit profit-making.

(cartoon from The Masses July 1916)

Faculty Lead: Sarah Deutsch

Project Manager: Evan Donahue

Optimizing Risk Assessment for Duke University Student Athlete Injury Prevention

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. This tool will produce actionable, student-athlete-specific reports that incorporate the analysis of previous injury history and current capabilities (K-Lab assessments) in order to identify injury risk and develop individualized recommendations for injury prevention. Students will develop analytic tools and scoring criteria to assess injury risk through profiling of data based on minimally clinically important differences, injury profiles, peer group analysis, and injury risk scoring strategies based on a comprehensive set of performance metrics. Injury risk identification will be furthered enhanced by clustering data analysis around joint or tissue specific injury risk, previous injury history, and athlete capabilities (strength, flexibility, and postural stability). The final deliverable will enhance injury prevention strategies for student-athletes and other populations by bridging the analytic gap between injury risk screening and actionable injury prevention strategies.

Faculty Lead: Dr. Tim Sell

Project Manager: Brinnae Bent

Recidivism in Durham County Jails

This team will collaborate with Durham’s Crisis Intervention Team, a group of law enforcement, fire, and EMS personnel who are specially trained to interact with citizens in mental health crisis.  We 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.  By the end of the summer, we will report findings and recommendations to the Crisis Intervention Team and Durham’s Stepping Up Initiative. 

Faculty Lead: Nicole Schramm-Sapyta, Michele Easter

Project Manager: Ruth Wygle

Invisible Adaptations: From Hamlet to the Avengers

Have you ever read or watched a movie and realized that you have seen the same story before?  How do you know if you are watching an adaptation? A team of students led by UNC-Chapel Hill graduate student Grant Glass, will develop means to track the movement of adaptations within contemporary culture through machine learning techniques. Drawing upon a variety of textual information drawn from historical and digital sources, the project team will have the opportunity to work with many different types of data. 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. By creating this training dataset, the team will use algorithms to identify adaptations in previously unidentified works. This will allow scholars to better understand at scale how certain narratives are adapted into new stories and forms.

Faculty Lead: Grant Glass

Project Manager: TBD

American Predatory Lending & the Global Financial Crisis

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), including (i) size of the market, (ii) composition of the market (conforming v. non-conforming), (iii) home ownership rates, (iv) originators (depository institutions v non-depositories), (v) default and foreclosure rates, (vi) assessments of the market by supervisory and regulatory agencies, (vi) press coverage of the mortgage market, and (vii) public statements by governmental leaders about home mortgages. Analyzing and presenting this data will allow the team to understand what information was publicly available to policymakers preceding the Crisis. The data will also be used to inform the oral histories of key policymakers that will be collected during a Bass Connections project that will begin in the fall of 2019.

Faculty Lead: Lee Reiners

Project Manager: Kate Coulter

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. By applying machine learning techniques to various speech datasets, students will identify features of human speech that can represent emotions, to develop software to perform emotion recognition, and to synthesize emotional speech data. Students also have the opportunity to create their own dataset, and apply their developed methods for training and testing. This work will allow further research along the direction of speech emotion analysis, and may result in new designs of human-computer interfaces.

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.

Click here to read the Executive Summary

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.

Click here for the Executive Summary

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.

Click here to read the Executive Summary

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.

Click here for the Executive Summary

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.

Click here to read the Executive Summary

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).

Click here to read the Executive Summary

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.

Click here to read the Executive Summary

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.

Click here to read the Executive Summary

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.

Click here to read the Executive Summary

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.

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.

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.

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.