A team of students led by researchers at the Duke Marine Lab will explore the changing distribution of krill around the Antarctic Peninsula. Krill are a key prey species in this ecosystem, supporting a number of animals including whales, seals, and penguins, but they are dependent on winter sea ice and may be in trouble as climate change progresses. Using data from acoustic zooplankton surveys, students will create maps and other products to visualize the spatial distribution of krill over the past 20 summers, then create metrics that allow us to quantify the way that krill distribution around the Antarctic Peninsula is changing as the climate shifts and ice melts. These results will be key to our understanding of the impacts of climate change on this polar ecosystem.

Project Lead: Douglas Nowacek

Project Manager: Amanda Lohmann

Today we design communication networks using mathematical models that describe components of the system that affect end-to-end performance. As wireless links become more highly variable, and system components become harder to model, this approach is losing ground. A team of students led by Dr. Robert Calderbank, Dr. Christ Richmond, Dr. Lingjia Liu, Dr. Jeff Reed, and Carl Dietrich will develop machine learning (ML) algorithms that take advantage of special features of new waveforms proposed for 6G wireless communication. The team will be highly interdisciplinary, and will include students from Virginia Tech familiar with wireless communication, as well as students interested in machine learning. Students will design experiments, collect data, and analyze over the air performance, some working onsite using Virginia Tech’s CORNET testbed (https://cornet.wireless.vt.edu/), some virtually using CORENT-based remote lab experiments. The team will present their findings to clients at the Air Force Research Lab in Rome, NY.

Project Leads: Robert Calderbank, Christ Richmond, Lingjia Liu, Jeff Reed, and Carl Dietrich 

Project Managers: Charles Connors and Akshay Bondre

Data+ has been in operation for 8 years, and several other linked programs have started up since, including Code+, which focuses on app development and CS+, which focuses on team-based research in Computer Science.
A team of students led by John Haws (OIT) will collaborate with Plus Programs administrators to review the data that has been gathered on students since each Plus Program began. The team will then make recommendations and create a single data structure and dashboard for Plus Programs that can be used for years to come to report on participants, suggest program improvements, and develop alumni outreach opportunities.

Project Lead: John Haws

Project Manager: Tigran Harutyunyan

The QSIDES Institute has pulled thousands of pages of police records from the Williamstown Police Department.  This project will analyze and identify patterns in police behavior in the town, work with journalists and other activists to use the data to develop action plans to address problems and enact public safety reform, and help to build an abstracted process and suite of tools that can be used by other small towns and municipalities to analyze their own data and empower police reform efforts across the nation. This project will interface with the Small Town Policing Accounting, or SToPA Lab. The overall goal for this project is to use the instance of the Williamstown Policing data to build out a toolkit that can be used in other small municipalities for policing transparency.
 

Project Leads: Jude Higdon, Greg Herschlag

Project Manager: Devin Cornell

Data+ students led by Prof. Henri Gavin will develop AI models for on-site earthquake early warning, in which sensors at a site provide warnings at that site. The Data+ project will integrate into ongoing work on geophone sensors, IOT microcontrollers, and networking. The Data+ team will focus on machine learning aspects of the project by making use of extensive seismic databases. The machine learning model, an anisotropic Gaussian Process, will relate waveform characteristics from the initial moments of shaking to predict the maximum expected shaking at the monitored site.

Project Lead: Henri Gavin

Project Manager: James Matuk, Ph.D.

A team of students led by Courtnea Rainey, David Jamieson-Drake, and Edward Balleisen will explore survey data from completers of the PhD and Duke PhD alumni to establish important correlations, document key patterns and longitudinal trends, and develop visualizations that can inform institutional decision-making. In addition to updating the work of last year’s Data+ team, this year’s group will use text mining and topic modeling to draw out key themes from the textual answers to free response questions.  Combining statistics with data science and natural language processing to analyze large datasets, this team will help refine our efforts to improve doctoral training across Duke University.

Project Lead: Ed Balleisen

Project Manager: Devin Cornell

A student team led by researchers at Duke Surgery and Global Health Institute will further develop the computer application - Alcohol Use Behavioral Phenotyping Test (AUBPT) that can help predict alcohol use and alcohol use disorder risks based on personal characteristics and behavioral performance on Research Domain Criteria paradigms/games. Students will build multi-tasking simulated AI agents using computational neuroscience and deep learning methods. These agents can 'mimic' human behaviors. Students will also use machine learning to model substance use risk from previously collected data. Together, these approaches will give a more rigorous understanding of causal relationships across behavioral paradigms and make AUBPT an adaptive application. This project is a part of the Bass Connections project - 'Alcohol Use Behaviors across Countries and Cultures' (https://bassconnections.duke.edu/project-teams/alcohol-use-behaviors-across-countries-and-cultures-2021-2022). The Bass Connections team is highly interdisciplinary where students are working at the interface of digital, mental, and global health to deployment, evaluation, and cultural adaptation of our AUBPT across multiple global samples.  The Bass Connections team is highly interdisciplinary where students are working at the interface of digital, mental, and global health for deployment, evaluation, and cultural adaptation of AUBPT across multiple global populations.

Project Lead: Siddesh Zadey, Dr. Catherine Staton and Dr. Joao Vissoci

Project Manager: Dalton Costa

A team of students led by Co-Principal Investigators Dr Jenny Immich and Dr Vicky McAlister will develop a geospatial methodology to automate data analysis originating from small unmanned aerial vehicles (SUAV) that seeks to identify the homes of ordinary medieval people within the modern Irish landscape. Known as aerial archeology, this work investigates archeological remnants on the surface of the earth without excavation. Students will create iterative Python scripts to analyze elevation datasets (hillshades) in areas surrounding medieval castles. Students will create advanced analysis methodologies for exploring low-lying archeological features in the landscape, including principal component, standard deviation, and difference mean elevations. Students with interest and appropriate backgrounds will be challenged to develop machine learning methods for identification of linear archeology. The resulting portfolio of work will be robust, including workflows, metadata, methodologies, and scripts. This work will solidify geospatial methodologies in advancing our knowledge of where everyday people lived in Ireland during the medieval period, a question that has long perplexed archeologists. In addition, it will fundamentally ground our understanding of complex topics in urbanization to the modern period and shed light on the development of communities across time. 

Project Lead: Vicky McAlister, Southeast Missouri State University

Project Manager: Harshit Sahay 

A team of students led by Biomedical Engineering professor Lingchong You will predict pattern formation of bacterial colonies by integrating experimental results with both mechanistic modelling and machine learning methods. Bacterial colonies have the capability to self-organize into beautiful and intricate patterns. Students will contribute to a method for controlling the outcome of colony spatial patterning, which is an important challenge facing the field of synthetic biology.

Project Lead: Lingchong You

Project Manager: Anita Silver

A team of students led by Statistical Science professors Mine Çetinkaya-Rundel and Maria Tackett will pull together all data associated with DataFest (https://www2.stat.duke.edu/datafest) for the purpose of retrospective archiving and documentation as well as creating a valuable resource that can serve as the one-stop-shop for students interested in participating in DataFest in the future. Goals of this project include curating “metadata” on past DataFests to build an interactive dashboard that shows the growth of the event (over time and over geographies) as well as highlights winning projects and participants, creating educational materials based on data from past DataFests that students can use to prepare for the event, and building a website that will serve as the student landing page for the global DataFest event and showcase all products of the Data+ projects. Students will develop their skills working with R packages like Shiny (to build the dashboard), blogdown (to build the website), and a large number packages for data wrangling, visualization, and modeling. All content developed as part of this project will be available to DataFest sites and over 3000 annual participants.

Project Leads: Mine Çetinkaya-Rundel and Maria Tackett

Project Manager: Jennifer Kampe

A team of students led by researchers Nicole Schramm-Sapyta (Duke Institute for Brain Sciences) and Maria Tackett (Statistical Science) will explore the impacts of community health services and local laws and policies on the justice-involved population in Durham. The team will create a public-facing interactive timeline of the implementation of mental health services, drug laws, and court policies in Durham; the data used to create the timeline will also be incorporated into the team’s ongoing analyses. Students will also analyze data from the Durham County Detention Center and Duke Health to explore the impact of the COVID-19 pandemic on incarceration and health-care utilization for the justice-involved population, along with the effects of policies related to mental illness and justice involvement. The data sets and analysis produced in Data+ will provide a foundation for the team’s research in Bass Connections during the 2022 - 2023 academic year.  Students are asked to commit to working with the team for Summer, 2022 (Data +) as well as Fall and Spring 2022-2023 (Bass Connections).

Project Leads: Nicole Schramm-Sapyta and Maria Tackett

Project Manager: Betsy Bersson

Using data from the Durham Compass and the NC School Report Card among many other sources, this team will continue the development of an interactive R Shiny dashboard that permits exploration of school statistical data.  The team aims to explore school zones through an asset-based lens in an effort to support ethical and imaginative partnerships between Duke, North Carolina Central, and Durham Public Schools.  This is a continuation of a joint Duke-NCCU Bass Connections project.

Project Lead: Alec Greenwald

Project Manager: Miko Liu

This data expedition focused on the mechanisms animals use to orient using environmental stimuli, the methods that scientists use to test hypotheses about orientation, and the statistical methods used with circular orientation data. Students collected their own data set during the class period, performed hypothesis testing on their data using circular statistics in R, and aggregated their data to formally test the hypothesis that isopods orient with light using an RShiny online application.

This exercise served as a capstone to a series of four class sessions on orientation and navigation, where students read primary scientific literature that used circular statistics in their methods. This data exercise was used to give students the opportunity to collect their own data, discover why linear statistics wouldn’t be sufficient to analyze them, and then implement their own analysis. The goal of this course was to give students a better understanding of circular statistics, with hands-on application in forming and testing a hypothesis.

Alexa Goble (Finance) joined Econ majors Chavez Cheong and Eli Levine in a ten-week exploration of mortgage enforcement actions related to the financial crisis from earlier in this century. Using NLP techniques on mortgage data from Ohio and Massachusetts, the team validated a new experimental approach to understanding the dynamics between state regulatory agencies, mortgage lenders, brokers, and loan originators. This project was a continuation of two previous Data+ projects:

https://bigdata.duke.edu/projects/american-predatory-lending-global-financial-crisis

https://bigdata.duke.edu/projects/american-predatory-lending-and-global-financial-crisis-year-2

View the team's project poster here

Watch the team's final presentation on Zoom:

Project Lead: Lee Reiners

Project Manager: Malcolm Smith Fraser

Pryia Juarez (BME/ECE), Jonathan Pilland (ECE/BME), and Matthew Traum (CS/Econ) spent teen weeks analyzing sensor data synthesized by an agile waveform generator. The team used deep reinforcement learning techniques to understand the performance of different synthetic agents representing potential attackers to the sensor system.

View the team's project poster here

Watch the team's final presentation on Zoom:

Faculty leads: Robert Calderbank, Vahid Tarokh, Ali Pezeshki

Client leads: Dr. Lauren Huie, Dr. Elizabeth Bentley, Dr. Zola Donovan, Dr. Ashley Prater-Bennette, Dr. Erin Trip

Project Manger: Suya Wu

Louis Hu (CS/Math), Fayfay Ning (Math/CS), and Kieran Lele (CS/Sociology) spent ten weeks exploring methods for exploring the similarities between networks of massive size, such as those arising from social media or from protein-protein alignment. The team used a variety of mathematical and software techniques and delivered a comprehensive analysis to experts in the field.

View the team's project poster here

Watch the team's final presentation on Zoom:

Project Lead: Jianming Xu

Project Manager: Sophie Yu

Caroline Tang (Math/Stats) joined CS majors Frankie Willard and Alex Kumar in a ten-week exploration of AI methods to improve the mapping of energy infrastructure within satellite imagery. The team used cutting-edge methods to create synthetic imagery that, when blended with real imagery, improved the performance of deep learning methods on the energy infrastructure detection task.

View the team's project poster here

Watch the team's final presentation on Zoom here:

Project Lead: Kyle Bradbury

Project Manager: Wei Hu

Simi Bleznak (Math/AI), Max Brown (Math/Econ), and Julia Choi (Bio) spent ten weeks Exploring how visual, cognitive, and physical abilities relate to physical performance can provide insight into the development of athletes. Using two rich datasets provided by USA Baseball, the team used linear regression, logistic regression models, and longitudinal methods to deliver key insights to decision-makers. This project was a continuation of a Data+ project from last summer: https://bigdata.duke.edu/projects/predicting-baseball-players%E2%80%99-athletic-performance

View the team's project poster here

Watch the team's final presentation on Zoom:

Project Leads: Marc Richard, Suhail Mithani, Greg Appelbaum

Project Mangers: Billy Carson and Hunter Klein

CS majors Aiman Haider and Divya Nataraj spent ten weeks analysing data and developing tools to assist the Fresh Produce Program (FPP) in achieving its core goals of creating a more equitable, sustainable, and integrated local food system. Using modern geospatial data analysis techniques, the team identified optimal locations for food distribution hubs, analyzed demographic data about the distribution area, and built an ArcGIS web app to allow interactive exploration of their results.

View the team's project poster here

Watch the team's final presentation on Zoom:

Project Lead: Willis Wong

Jayesh Gupta (ECE/CS), Trevon Helm (ECE), and Yvonne Kuo (CS/PoliSci) spent ten weeks developing tools to extract features from network data in collaboration with information security professionals with Duke’s OIT. The team performed exploratory data analysis to extract features and used machine-learning techniques to detect zero-day attacks. This project was sponsored by Cisco, Inc.

View the team's project poster here

Watch the team's final presentation on Zoom:

Project Lead: Eric Hope

Project Manager: Pranav Manjunath

Molly Borowiak (CS) and Joshua Tennyson (CS/BME) spent ten weeks building tools to assist in the analysis of data arising from microbial growth experiments. The team produced a comprehensive Python package that enables the exploration of a variety of modeling techniques with the data.

View the team's project poster here

Watch the team's final project presentation on Zoom:

(photo credit: Peter Tonner et al., 2020 PLoS Computational Biology https://journals.plos.org/ploscompbiol/article/comments?id=10.1371/journal.pcbi.1008366 )

Project Lead: Amy Schmid

Project Manager: David Buch

After London was destroyed during the Great Fire of 1666, it was reconstructed into the “emerald gem of Europe,” a utopian epicenter focused on England’s political and economic interests. For whom was the utopia constructed? Who determined its architectural choices? And what did such a utopia look like in seventeenth-century London?

Our research uses Natural Language Processing to analyze semantic trends in digitized text from the online database “Early English Books Online” (EEBO-TCP https://textcreationpartnership.org/tcp-texts/eebo-tcp-early-english-books-online/) to answer such questions. After applying methods such as word-embedding, sentiment analysis, and hapax richness, we provide an overview of themes in the seventeenth century; specifically, we conducted case studies on changes to coal taxes within the period and the reconstruction of St Paul's Cathedral. Our results thus show that, while a utopian society was originally intended to be built for the people, the project’s motivation eventually shifted to a political purpose, as evidenced by the approval of more costly city projects. In response to backlash against the increase of taxes on coal to support large-scale building projects, the ruling class highlighted positive outcomes in printed materials in order to convince working class persons that their collected taxes contributed to a greater good, despite evidence to the contrary. Finally, during key historical events, sentiment and hapax richness are shown to have an inverse relationship, the results of which can demonstrate how London writers engaged with text and genre as forms of protest.

View the team's project poster here

Watch the team's final project presentation on Zoom:

Brianna Cellini (Global Health, Neuroscience), Kexin (William) Feng (Public Policy, Philosophy), John Liakos (Neuroscience), and Maya Pandey (Political Science, Public Policy) received updated data from the Durham county sheriff’s office and Duke health, adding data from 2019-2020.  They spent the summer cleaning that data, comparing past demographics and descriptive statistics to the new data, and preparing to analyze the effect of COVID and new Durham County policies on incarceration and health service utilization.  They also established a new criterion to indicate which patients experience severe mental illness, based on health system records, to complement the mental health tagging system from the detention facility.

View the team's project poster here

View the team's final presentation on Zoom:

Project Lead: Nicole Schramm-Sapyta

Project Manager: Ruth Wygle

Is there a right type and amount of consumption?

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.  

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.

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.

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.

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.

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.

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.

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

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.

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:

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.

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:

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

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.

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).

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

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

 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

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

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

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.

Click here to read the Executive Summary

Faculty Lead: Nicole Schramm-Sapyta, Michele Easter

Project Manager: Ruth Wygle

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.

Click here to read the Executive Summary

Faculty Lead: Lee Reiners

Project Manager: Kate Coulter

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.

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.

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.

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

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.

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

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

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

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

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.

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

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

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

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

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

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.

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

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

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

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

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

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

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.