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...
A team of students led by researchers at the Duke Center for Policy Impact in Global Health (CPIGH) will create a user-friendly interactive visualization tool to track the evolution of Universal Health Coverage (UHC) financing policies in the low- and middle-income countries. The students will use the UHC policy surveillance...
This Data+ project puts you on the front lines of a real question ministries and funders are facing right now: should we scale this AI health tool—or shut it down? You’ll work with real implementation data from global health innovators to clean, validate, and turn messy reality into decision-ready visuals....
Ashley Murray (Chemistry/Math), Brian Glucksman (Global Cultural Studies), and Michelle Gao (Statistics/Economics) spent 10 weeks analyzing how meaning and use of the work “poverty” changed in presidential documents from the 1930s to the present. The students found that American presidential rhetoric about poverty has shifted in measurable ways over time. Presidential rhetoric, however, doesn’t necessarily...
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 modeling 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...
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...
This project aims to analyze assessment and performance data collected from baseball players to make predictions about baseball performance based on vision and physical abilities. We use hierarchical regression analyses to identify characteristics that correlate with batting performance in order to inform scouts about the likely production of developmental prospects....
This project involves predicting the incidence of blindness in glaucoma patients at Duke Eye Center (DEC) — specifically, the likelihood of a patient presenting legally blind (i.e. with very advanced disease) at their first visit. We will assemble a novel data set of electronic health records from thousands of DEC...
The goal of this Data+ project was to apply and extend custom analytics solutions to understand and predict microbial population growth. An explosion of data has resulted from tracking the growth of bacteria in high throughput devices. These data were generated to understand how microbes grow. Better models that fit...
The goal of this Data+ project is to apply and extend custom analytics solutions to understand and predict microbial population growth. An explosion of data has resulted from tracking the growth of bacteria in high throughput devices. These data were generated to understand how microbes grow. Better models that fit...
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....
Despite the vast amount of admissions data we collect, there is still limited understanding of what factors are most predictive of admitted students’ decision to attend Duke. Traditional yield models have focused on a small set of variables that have limited power in predicting students’ decisions, and there may be...
Fluid mechanics is the study of how fluids (e.g., air, water) move and the forces on them. Scientists and engineers have developed mathematical equations to model the motions of fluid and inertial particles. However, these equations are often computationally expensive, meaning they take a long time for the computer to...
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...
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...
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...
Yanmin (Mike) Ma, mathematics/economics major, and Manchen (Mercy) Fang, electrical and computer engineering/computer science major, spent ten weeks studying historical archives and building a model to predict the price of pigs, relative to a number of interesting factors. Project Results Berkshire pig transaction price is influenced by factors including the age of...
The Protecting American Investors project investigates the evolving structure and content of financial advice from the early 20th century to the birth of the Internet. By converting and cleaning thousands of investment advice columns from historical newspapers and magazines, we assembled a large corpus to address our research questions. Through...
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. Project Results: The team showed that movies which pass the Bechdel...
A team of students led by Duke mathematician Marc Ryser and University of Southern California Pathology professor Darryl Shibata will characterize phenotypic evolution during the growth of human colorectal tumors. Students will perform an in-depth investigation of phenotypic conservation at multiple functional levels in epigenomic methylation data – including CpG sites, genes, and functional groups within...
Showing 281-300 of 388 results