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.

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 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.

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 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. 

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 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. 

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.

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.

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.

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.

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.

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.

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

Faculty Instructor: Colin Rundel

Class: STA 112, Data Science

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.

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. 

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.