Nationally there is a disproportionate number of children of color (African American & Latino) in the child welfare system. Durham County is no different. However, reviewing this problem through the lens of data has not been done to formulate or implement possible solutions. Durham County Department of Social Services Child & Family Services would like to evaluate systems to identify where and how disproportionality and disparity are occurring. It is occurring at the entry point of Reporting child abuse and neglect? Is it occurring at the case decision? Is our reunification time different for African American children? Or Does it take longer for a child of color to achieve permanence through adoption? Organizing the data to show us our “hot spots” would facilitate further discussion and focus on solutions to an age-old systemic problem.

Faculty Lead: Greg Herschlag

Project Lead: Jovetta L Whitfield

A team of students will use visual reporting tools, such as Tableau or Power Bi, to create a dynamic dashboard that will enable investment professionals at Duke University Management Company (DUMAC) to review and better understand fund managers’ exposures and positioning across various dimensions.  Students will collaborate with teams at DUMAC to develop an intuitive, visual dashboard to help the investment team review individual and portfolio-level exposures across numerous asset classes.  This dashboard will be connected to DUMAC’s data warehouse, which refreshes on a daily basis as new data comes in for DUMAC’s public market accounts.

Finding similarity and node correspondence among networks has been widely applied in social network analysis, network de-anonymization, protein-protein interaction (PPI) network alignment, and shape matching in computer vision. A team of students led by Professor Jiaming Xu and Ph.D. student Sophie Yu will implement our recently developed graph matching and similarity scoring algorithms, test their empirical performance in real datasets, and further improve the algorithm design by incorporating domain knowledge. Multiple datasets will be explored such as Facebook ego network, Covid PPI network, Isobase PPI network, computer vision datasets, and Wikipedia article networks. A visualization app will be built to facilitate researchers and practitioners to apply graph matching in relevant applications.

Along with the Energy Data Analytics Lab and the Sustainable Energy Transitions Initiative, students will explore how machine learning can be developed to better identify and characterize energy infrastructure and scale up its application across geographies. Using synthetic data generation such as style transfer and/or 3D models to create images of artificial cities and communities, we will examine how well an algorithm trained on one set of training data is able to adapt to new places and types of energy infrastructure. This research will increase the speed and scale of assessment towards an automated, global assessment of energy infrastructure. The resulting data would empower decisionmakers and energy system planners to make better decisions for planning electricity system development for communities with limited electricity access or reliability.

The goal of this project is to use evaluation data collected on baseball athletes to make predictions about their on-field performance in competitive games. Evaluation data includes visual skills assessed with eye tracking, movement screening data and simulated batting performance metrics collected in virtual reality that are measured as part of the USA Baseball Prospect Development Pipeline. Using regression and Bayesian statistical analyses we will identify characteristics of these data that correlate with batting performance in order to inform scouts about the likely production of developmental prospects. The final product will be an application that uses an athlete's assessment results to produce performance summary graphs for the individual compared to other athletes and inferential models for the relationships between assessments and performance.

Project Leads: Marc Richard, Suhail Mithani, Greg Appelbaum

The Root Causes Fresh Produce Program, led by an interdisciplinary team of graduate and undergraduate students at Duke and UNC, leverages the power of student volunteers and community partnerships to address food insecurity in our community by delivering fresh and locally sourced produce to the doorsteps of patients in the Duke, Lincoln, and Samaritan Community health systems. Our program leadership is looking for team members to join us in exploring the use of data visualization and analysis to improve upon the processes that allow us to carry out our student-run service. Specific projects will include improving the program’s delivery route optimization software, improving the crop assessment and inventory management tools of small farmers and wholesale markets, as well as developing a dashboard integrating client location data and GIS information to identify location-specific service providers. Together, in collaborating with our team of interdisciplinary students as well as local Durham agencies, we hope to give you an opportunity to make an impact on our local community and food system while gaining a deeper appreciation for the role of data visualization and analysis through a population health intervention.

Project Lead: Willis Wong

A team of students led by members of the Duke IT Security Office will develop machine learning features that can be used to identify previously unknown web attacks.  Attacks targetting websites and web applications are continuous across the internet and protecting Duke's web infrastructure is part of the IT Security Office's mission.  The team will have a large dataset of logs from web traffic to explore possible features and evaluate their usefulness for identifiying new types of web attacks.  The results from this project may be incorporated into Duke's IT security infrastructure to help protect Duke's network in the future.

Project Lead: Eric Hope

The goal of this Data+ project is to apply and extend custom analytics solutions to discover how life remains resilient in extreme environments. An explosion of data has resulted from recent discoveries of tiny single-celled life hiding out in the most extreme places on Earth. These single celled creatures, or microbes, thrive in hot springs at boiling temperatures, in brine lakes saturated with salt, and in deserts once thought to be sterile. Studies in the Schmid lab at Duke have generated large data sets tracking how microbes grow and change their gene expression when faced with extreme stress. However, the majority of these data remain to be analyzed. In this Data+ project, students will apply and extend the statistical models to ask how extreme microbes grow and change during exposure to nutrient deprivation, extreme stress, and genetic mutations.

(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 Schmidt

Project Manager: David Buch

Who should get to decide what a utopian society looks like? After London was razed to the ground in the Great Fire of 1666, its reconstruction into the “emerald gem of Europe” was heavily influenced by the monarchy and aristocratic elites. In building a utopian epicenter focused on political and economic interests, immense sacrifices had to be made by London’s most marginalized citizens. A team of students led by Nicholas Smolenski (PhD Candidate, Musicology) and Dr. Astrid Giugni (Lecturing Fellow, English) will thus explore how London was rebuilt into a utopia; by employing topic modeling and applying the resulting lexicon to seventeenth-century architectural sketches, students will demonstrate how a language of progress became inextricably linked to its own image while also exposing the paradoxes entrenched in utopic representation. This project will additionally show how its framework can apply to current political discourse, as the tearing down of statues and monuments over the past three years has highlighted inescapable tensions between a governing power, a nation’s history, and its people.

Mental illness is often over-represented among the incarcerated population.  Durham county leaders are aware of this, and have taken many steps in recent years to support incarcerated people with mental illness and also to reduce incarceration in this population.  This work requires a community-wide effort, and Duke University Health System, as the major health care provider in the county, has a potential role to play. This Data + team will use data from the Durham County Detention Facility and Duke Health System to examine patterns of health-service utilization in the incarcerated population, including those with and without mental illness diagnoses.  We will use statistical methods such as longitudinal modeling to analyze the effects of the many interventions recently implemented, including increased mental health services, medication-assisted treatment for addiction, among others.  

Project Lead: Nicole Schramm-Sapyta

Project Manager: Ruth Wygle

Is there a right type and amount of consumption? The idea of ethical consumption has gained prominence in recent discourse, both in terms of what we purchase (from fair trade coffee to carbon off-sets) and how much we consume (from rechargeable batteries to energy efficient homes). Concern with the morality of consumption is not new to capitalist societies; we can find debates on the ethics of consumption in the Middle Ages and the Renaissance. A team of students led by Dr. Astrid Giugni (English, Duke) and Dr. Jessica Hines (English, Birmingham Southern College) will analyze approximately 60,000 Medieval and Renaissance digitized texts by performing topic modelling, which will allow them to track associations between the languages of consumer culture and ethical practice.The team will organize their results in a series of visualizations that trace the development of the ethics of ‘consumerism’ in terms of frequency of use, timeline of relevant events, authorship, location of publication and name of printer/printing press, and genres.

The Air Force’s F-15E Strike Eagle jets have parts that wear down and break, causing unscheduled maintenance events that take away valuable time in the air for critical missions and training. Our team, Limitless Data, is working with Seymour Johnson Air Force Base to mine manually entered maintenance data to visualize and predict aircraft failures. We created a prototype data visualization product that will enable maintainers on the flight line and help them identify and repair critical failures before they happen, keeping jets ready to fly, fight and win.

Mapping History has focused on the categorizing, labelling, digitization, and 3D reconstruction of 16^th & 17^th 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.

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.

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.

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.

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.

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.

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:

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.

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

Our project is about building a food recommendation system for Avoidant/Reactive Intake Disorder (ARFID) patients and understanding the relationship between ARFID and clinical variables. Our stakeholders include young picky eaters and their parents, as well as clinicians who work with ARFID patients. We created an interactive visualization for ARFID patients to encourage them to explore different foods and also built visualization to represent the relationship between ARFID and clinical variables.

Project Leads: Guillermo Sapiro, Nancy Zucker

Project Manager: Julia Nichols

Interact with the team's visualization tool here: http://foodrecbucket.s3-website-us-west-1.amazonaws.com/

View the team's final project presentation slides here

Watch the team's final presentation below:

Traditional Human Activity Recognition (HAR) utilizes accelerometry (movement) data to classify activities. This summer, Team #4 examined using physiological sensors to improve HAR accuracy and generalizability. The team developed ML models that are going to be available open source in the Digital Biomarker Discovery Pipeline (DBDP) to enable other researchers and clinicians to make useful insights in the field of HAR.

Project Lead: Jessilyn Dunn

Project Manager: Brinnae Brent

Click here to view the project team's project poster

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

Aaron Chai (Computer Sciece, Math) and Victoria Worsham (Economics, Math) spent ten weeks building tools to understand characteristics of successful oil and gas licenses in the North Sea. The team used data-scraping, merging, and OCR method to create a dataset containing license information and work obligations, and they also produced ArcGIS visualizations of license and well locations. They had the chance to consult frequently with analytics professionals at ExxonMobil.

Click here to read the Executive Summary

Project Lead: Kyle Bradbury

Project Manager: Artem Streltsov

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

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

Katelyn Chang (Computer Science, Math) and Haynes Lynch (Environmental Science, Policy) spent ten weeks building tools to analyze and visualize geospatial and remote sensing data arising from the Alligator River National Wildlife Refuge (ARNWR). The team produced interactive maps of physical characteristics that were tailored to specific refuge management professionals, and also built classifiers for vegetation detection in LandSat imagery.

Click here to read the Executive Summary

Faculty Leads: Justin Wright, Emily Bernhardt

Project Manager: Emily Ury

Nathaniel Choe (ECE) and Mashal Ali (Neuroscience) spent ten weeks developing machine-learning tools to analyze urodynamic detrusor pressure data of pediatric spina bifida patients from the Duke University Hospital. The team built a pipeline that went from raw time series data to signal analysis to dimension reduction to classification, and has the potential to assist in clinician diagnosis.

Click here to read the Executive Summary

Faculty Leads: Wilkins Aquino, Jonathan Routh

Project Manager: Zekun Cao

Yoav Kargon (Mechanical Engineering) and Tommy Lin (Chemistry, Computer Science) spent ten weeks working with data from the Water Quality Portal (WQP), a large national dataset of water quality measurements aggregated by the USGS and EPA. The team went all the way from raw data to the production of Pondr, an interactive and comprehensive tool built with R Shiny that permits users to investigate and visualize data coverage, values, and trends from the WQP.

Click here to read the Executive Summary

Faculty Lead: Jim Heffernan

Project Manager: Nick Bruns

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

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 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

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

Vivek Sahukar (Masters, Data Science), Yuval Medina (Computer Science), and Jin Cho (Computer Science/Electrical & Compter Engineering) spent ten weeks creating tools to help augment the experience of users in the StreamPULSE community. The team created an interactive guide and used data sonification methods to help users navigate and understand the data, and they used a mixture of statistical and machine-learning methods to build out an outlier detection and data cleaning pipeline.

Click here to read the Executive Summary

Faculty Leads: Emily Bernhardt, Jim Heffernan

Project Managers: Alice Carter, Michael Vlah

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?

Cassandra Turk (Economics) and Alec Ashforth (Economics, Math) spent ten weeks building tools to help minimize the risk of trading electricity on the wholesale energy market. The team combined data from many sources and employed a variety of outlier-detection methods and other statistical tools in order to create a large dataset of extreme energy events and their causes. They had the opportunity to consult with analytics professionals from Tether Energy.

Click here to read Executive Summary

Project Lead: Eric Butter, Tether

Brooke Erikson (Economics/Computer Science), Alejandro Ortega (Math), and Jade Wu (Computer Science) spent ten weeks developing open-source tools for automatic document categorization, PDF table extraction, and data identification. Their motivating application was provided by Power for All’s Platform for Energy Access Knowledge, and they frequently collaborated with professionals from that organization.

Click here to read the Executive Summary

Maksym Kosachevskyy (Economics) and Jaehyun Yoo (Statistics/Economics) spent ten weeks understanding temporal patterns in the used construction machinery market and investigating the relationship between these patterns and macroeconomic trends.

They worked closely with a large dataset provided by MachineryTrader.com, and discussed their findings with analytics professionals from a leading asset management firm.

Click here to read the Executive Summary

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

David Liu (Electrical Computer Engineering) and Connie Wu (Computer Science/Statistics) spent ten weeks analyzing data about walking speed from the 6th Vital Sign Study.

Integrating study data with public data from the American Community Survey, they built interactive visualization tools that will help researchers understand the study results and the representativeness of study participants.

Click here to read 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.

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

Dima Fayyad (Electrical & Computer Engineering), Sean Holt (Math), David Rein (Computer Science/Math) spent ten weeks exploring tools that will operationalize the application of distributed computing methodologies in the analysis of electronic medical records (EMR) at Duke.

As a case study, they applied these systems to an Natural Language Processing project on clinical narratives about growth failure in premature babies.

Click here to read the Executive Summary

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

Bob Ziyang Ding (Math/Stats) and Daniel Chaofan Tao (ECE) spent ten weeks understanding how deep learning techniques can shed light on single cell analysis. Working with a large set of single-cell sequencing data, the team built an autoencoder pipeline and a device that will allow biologists to interactively visualize their own data.

Click here to read 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

Sophie Guo, Math/PoliSci major, Bridget Dou, ECE/CompSci major, Sachet Bangia, Econ/CompSci major, and Christy Vaughn spent ten weeks studying different procedures for drawing congressional boundaries, and quantifying the effects of these procedures on the fairness of actual election results.

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.

Sharrin Manor, Arjun Devarajan, Wuming Zhang, and Jeffrey Perkins explored a lage collection of imagery data provided by the U.S. Geological Survey, with the goal of identifying solar panels using image recognition. They worked closely with the Energy Data Analytics Lab, part of the Energy Initiative at Duke.

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.

Computer Science major Yumin Zhang and IIT student Akhil Kumar Pabbathi spent ten weeks working closely with Dr. Joe McClernon from Psychiatry and Behavioral Sciences to understand smoking and tobacco purchase behavior through activity space analysis.

Biomedical Engineering and Electrical and Computer Engineering major David Brenes, and Electrical and Computer Engineering/Computer Science majors Xingyu Chen and David Yang spent ten weeks working with mobile eye tracker data to optimize data processing and feature extraction. They generated their own video data with SMI Eye Tracking Glasses, and created computer vision algorithms to categorize subject gazing behavior in a grocery purchase decision-making environment.

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.

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.

Yuangling (Annie) Wang, a Math/Stats major, and Jason Law, a Math/Econ major, spent ten weeks analyzing message-testing data about the 2015 Marijuana Legalization Initiative in Ohio; the data were provided by Public Opinion Strategies, one of the nation's leading public opinion research firms.

The goal was to understand how statistics and machine learning might help develop microtargeting strategies for use in future campaigns.

Devri Adams (Environmental Science), Annie Lott (Statistics), and Camila Vargas Restrepo (Visual Media Studies, Psychology) spent ten weeks creating interactive and exploratory visualizations of ecological data. They worked with over sixty years of data collected at the Hubbard Brook Experimental Forest (HBEF) in New Hampshire.

Robbie Ha (Computer Science, Statistics), Peilin Lai  (Computer Science, Mathematics), and Alejandro Ortega (Mathematics) spent ten weeks analyzing the content and dissemination of images of the Syrian refugee crisis, as part of a general data-driven investigation of Western photojournalism and how it has contributed to our understanding of this crisis.

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.

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.

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

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

Selen Berkman (ECE, CompSci), Sammy Garland (Math), and Aaron VanSteinberg (CompSci, English) spent ten weeks undertaking a data-driven analysis of the representation of women in film and in the film industry, with special attention to a metric called the Bechdel Test. They worked with data from a number of sources, including fivethirtyeight.com and the-numbers.com.

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.

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.

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.

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.

Gary Koplik (Masters in Economics and Computation) and Matt Tribby (CompSci, Statistics) spent ten weeks investigating the burden of rare diseases on the Duke University Health System (DUHS). They worked with a massive set of ICD diagnosis codes and visit data provided by DUHS.

Over ten weeks, Biology major Jacob Sumner and Neuroscience major Julianna Zhang joined forces with Biostatistics Masters student Jing Lyu to analyze potential drug diversion in the Duke Medical Center. Early detection of drug diversion assists health care providers in helping patients recover from their condition, as well as mitigate the effects on any patients under their care.

Albert Antar(Biology), and Zidi Xiu (Biostatistics) spent ten weeks leveraging Duke Electronic Medical Record (EMR) data to build predictive models of Pancreatic ductal adenocarcinoma (PDAC). PDAC is the 4th leading cause of cancer deaths in the US, and is most often is diagnosed in stage IV, with a survival rate of only 1% and life expectancy measured in months. Diagnosis of PDAC is very challenging due of deep anatomical placement, and significant risk imposed by traditional biopsy. The goal of this project is to utilize EMR data to identify potential avenues for diagnosing PDAC in the early treatable stages of disease.

Vivek Sriram (Computer Science and Math), Lina Yang (Biostatistics), and Pablo Ortiz (BME) spent ten weeks working in close collaboration with the Department of Biostatistics and Bioinformatics implementing an image analysis pipeline for immunofluorescence microscopy images of developing mouse lungs.

Emily Horn (Public Policy, Global Health), Aasha Reddy (Economics), and Shanchao Wang (Masters Economics) spent ten weeks working with data from the National Asset Scorecard for Communities of Color (NASCC), an ongoing survey project that gathers information about asset and debt of households at a detailed racial and national origin level. They worked closely with faculty and researchers from the Samuel Dubois Cook Center for Social Equity.

Joel Tewksbury (BME) and Miriam Goldman (Math and Statistics, Arizona State University) spent ten weeks analyzing time-series darkness visual adaptation scores from over 1200 study participants to identify trends in night vision, and ultimately genetic markers that might confer a visual advantage.

Lindsay Hirschhorn (Mechanical Engineering) and Kelsey Sumner (Global Health and Evolutionary Anthropology) spent ten weeks determining optimal vaccination clinic locations in Durham County for a simulated Zika virus outbreak. They worked closely with researchers at RTI International to construct models of disease spread and health impact, and developed an interactive visualization tool.

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.

Undergraduate students Ellie Burton (BioPhysics/Math, Johns Hopkins University), Kevin Kuo (Electrical and Computer Engineering), and GiSeok Choi (Electrical and Computer Enhineering/Math) joined a research group led by Douglas Boyer and Professor Ingrid Daubechies, testing and developing mathematical and statistical methodology for measuring similarities between bones and teeth.

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.

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.