Research

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

A team of students will work with Duke’s Office of Information Technology to conceptualize and potentially develop an “e-advisor” program that will help students navigate, augment, and map their way through Duke’s co-curricular ecosystem. The team of students will identify available data, programs and resources, define learning objectives, recommend common pathways and create a storyboard of the program building out a “master narrative” experience and prototype the branching and decision engine. Students will work with de-identified registration and advising data in a secure environment, have access to the analytics tools used in OIT, and will have an opportunity for exploration of the data in consultation with OIT and data analytics professionals.

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

Today, our society is struggling with an unprecedented amount of misinformation and disinformation. A team of students led by researchers in the Duke Reporters’ Lab and Department of Computer Science will build databases, systems, and apps to help fact-checkers combat falsehoods and hyperboles, and disseminate their fact-checks to the public. The team will apply database, machine learning, algorithmic, and app development techniques to scout media and public interest for check-worthy claims, and alert media consumers to previously checked claims instantly.

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.

A team of students led by faculty and researchers at the Social Science Research Institute will bring together data that will facilitate research using social determinants of health (SDH) to examine, understand, and ameliorate health disparities. This project will identify SDH variables that have the potential to be linked to data from the MURDOCK Study, a longitudinal health study based in Cabbarus County, NC. Much of this data – information relevant to understanding socioeconomic status, education, the physical and social environment, employment, and social support networks – is publicly available or easily obtained and its aggregation and analysis offer opportunities to significantly improve predictions of health risks and improve personalized care. Students will evaluate potential data sources, develop ethical policies to protect respondent privacy, clean and merge data, create documentation for data sharing and reuse, and use statistical tools and neighborhood mapping software to examine patterns of disparity.

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

How are women influenced by the spaces that they are allowed to occupy? A group of students, led by English Professor Charlotte Sussman, will examine how the spaces and places women can inhabit have changed over time, and how such changes have affected women’s rights and opportunities. The team will analyze the visual representations of women depicted in magazines from the nineteenth to the twenty-first century through the Women’s Magazine Archive, considering how images about women influence the reality that women can both imagine and live. Using this data, the group will design and visualize a potential women’s space that can empower and support women to reach their highest potential.

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

A team of students led by Glenn Elementary School Parent Teacher Association (PTA) President, David Vanie, will explore publicly available data in order to develop a set of metrics that serve to understand the needs of the GSE parent community in a holistic way.  The data will identify potential obstacles that are barriers for parent involvement, and will inform best practices for increasing participation throughout the 2018-2019 school year at GSE.  The work will be used to provide helpful insight for engaging parents in PTA organizations at public schools throughout Durham, and across the country. 

 

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

What do we mean by the term “poverty”?

A team of students under the direction of Professor Astrid Giugni will analyze how the way we talk about poverty and public policy has changed over time. The team will work with two databases containing visual, textual, and audio documents from 1473 to the present, allowing students to track and analyze how our understanding of poverty has changed over time. The group will tackle the challenge of analyzing the political and popular language and imagery of poverty in order to create a visualization that contextualizes how financial and welfare policy is influenced by how we talk about poverty.

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

 

 A team of students lead by Dr. Nicole Schramm-Sapyta of the Duke Institute for Brain Sciences will provide analytical consulting support to the Durham Crisis Intervention Team (CIT) Collaborative, a county-wide effort to provide law enforcement and first responders with specialized training in mental illness and crisis intervention techniques.  The team will build on last summer’s descriptive analysis of 9-1-1 call data by incorporating data from partner agencies to assess whether CIT training reduces recidivism, increases utilization of mental health services, and generally improves the lives of Durham citizens with mental illness. 

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

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 RaskopfPoliSci 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.

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

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.

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.

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

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+ teamYu 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.

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.

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, 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.

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

Faculty Instructor: Colin Rundel

Class: STA 112, Data Science

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

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.

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.

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

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. 

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

Inspiring and empowering donors to give more effectively

We want three bright, motivated students to participate in this nine-week Data+ project!

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

Kelsey SumnerEvAnth 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 LevineAnnie 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.