A multidisciplinary team of students will work at the intersection of data science, policy, and food systems to analyze critical agricultural research shaping the science of food, agriculture, and the environment in the United States. Using natural language processing and machine learning, the team will explore federal grant records to...
A team of students, collaborating with Professors Mike Bergin, David Carlson, and PhD Candidate Zach Calhoun will develop a modeling approach to estimate heat stress in urban areas. Students will further develop a dataset consisting of high-resolution temperature and relative humidity observations in over 60 cities (https://www.heat.gov/pages/mapping-campaigns), satellite imagery and...
A team of students led by Ph.D. student Yu Wei and assistant professor Tong Qiu from the Spatial Ecology and Environmental Data Sciences (SEEDS) lab will utilize cutting-edge remote sensing technologies—including hyperspectral imagery and airborne Light Detection and Ranging (LiDAR)—combined with an advanced deep learning framework to enhance forest biodiversity...
This is an innovative project that explores the intersection of artificial intelligence and mathematics. This initiative aims to leverage AI’s capabilities in pattern recognition and exhaustive search to tackle complex problems in discrete mathematics, such as finding counterexamples to open conjectures. By framing these mathematical challenges as computational problems, students...
Duke has a new Chief Engagement Officer, a Double Dukie, who is passionate about advancing and optimizing alumni engagement, involvement, and experience. With a private sector performance marketing and media background, she hopes to lead a team of students, in collaboration with Professor Shep Moyle (a Duke alum, former Duke...
Students will create accessible, actionable data on climate risks and climate resilience efforts in the Milwaukee River area. By harnessing existing data sources as inputs for hazard modeling, focused on flooding, students will use techniques to account for uncertainties in inputs, providing more accurate and adaptable risk assessments for communities....
Online data scraping has reached a fever pitch, as AI creators seek food for their hungry models. Researchers from the Argus Lab at Duke are building tools to analyze web scraping at scale based on analysis of Duke’s web logs. Data+ students will investigate the time-scale of AI data scraping (e.g....
A team of students under Biomedical Engineering professor Megan Madonna will explore the connection between young students’ sense of belonging in a community and their ability and motivation to solve hard problems related to their community. We are interested in studying who and why people become and remain engineers. The...
A team of student researchers will collaborate with the Southeast Regional Coalition for University-Assisted Community Schools (SRCUACS) and the North Carolina Community Schools Coalition (NCCSC) to examine how statewide policies have impacted funding for public schools throughout history. The team will create a dashboard to illustrate the correlation between per-pupil funding and enrollment and...
A team of students will collaborate with a doctoral fellow in Computational Humanities and the Asian American Studies Librarian to develop a statistics-based Natural Language Processing toolkit to study the linguistic styles of Asian American short stories published between 1974 and 2024. This toolkit will support historians, literary scholars, and...
A team of students led by researchers in the Durham Public Schools planning department will analyze, map, and visualize student enrollment and demographic data in Durham County. Potential projects for next summer include working with Census and DPS data to develop models that explain DPS enrollment trends historically and across...
A team of students led by an interdisciplinary group including statistician Fan Li, neurologist Brian Mac Grory, and preventive medicine physician/clinical data scientist Jay Lusk will integrate information from diverse real-world datasets to better understand risk factors for cardiovascular diseases such as heart attack and stroke and for cognitive disorders...
The application of deep learning to Alzheimer’s disease (AD) research using MRI is a rapidly evolving field, with existing studies serving primarily as proof of concept. This Data+ project aims to contribute to the development of a deep learning model that integrates MRI-based topological biomarkers for the early detection of...
A team of students led by staff from the Duke Office of Climate and Sustainability will explore improvements to Duke University’s greenhouse gas data system. Students will see how the data flows through campus and how it is ultimately used to quantify and report on Duke’s contribution to climate change...
Students will curate administrative data to conduct Sequence Analysis (SA), a technique used to analyze patterns in sets of categorical sequences over time. Traditional education reports often rely on cross-sectional data (e.g., proportion of students under economic disadvantage), and in tend to overlook the chronic exposure to disadvantages that longitudinal...
A team of students led by researchers in the Energy Data Analytics Lab and the Sustainable Energy Transitions Initiative will develop a method to evaluate electricity access in developing countries through machine learning techniques applied to aerial imagery data. Students will first improve the accuracy of the solar array identifying...
Is it ethically permissible to sell, buy, and use luxury goods? What labor practices do we tolerate to make these goods available? In the late Middle Ages and Renaissance, England was faced with an ever-growing supply of new and exciting goods, made possible by new trade routes to the New...
A team of researchers associated with the Applied Machine Learning Laboratory will lead a team of students in developing novel machine learning techniques that will be used for improving brain computer interfaces (BCIs) using electroencephalography (EEG) data. Students will learn how to pre-process EEG data, extract EEG features, and train...
Join our team in creating “The Survey Navigator,” an interactive platform that helps users discover, compare, and visualize public opinion data. Led by professors Sunshine Hillygus and Alexander Volfovsky, and supported by Duke’s Polarization Lab, we will harness the power of statistics, machine learning, and AI to transform raw survey...
A group of students, guided by climate science and environmental engineering professors, will use deep learning models to enhance flash flood predictions in the Southeastern United States. They will study extreme weather events that contribute to flooding and learn to identify these events using satellite and radar imagery. By applying...
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