2023 Projects
A team of students collaborated with researchers from the National Diaper Bank Network / the Diaper Bank of North Carolina (Kelley Massengale) and Duke Department of Pediatrics (Rushina Cholera) to assess characteristics of diaper bank recipient families. Utilizing the largest dataset of information provided by families accessing diaper banks, students assessed trends and shared characteristics […]
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A team of students analyzed, mapped, and visualized student enrollment and demographic data in Durham County. The team collaborated closely with analytics professionals at the Durham Public Schools (DPS) operations department and provided analysis to help DPS plan and manage student enrollment and improve fairness in its student assignment policies. Project Lead: Vitaly Radsky, School […]
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A team of students led by Physics professor Dan Scolnic collaborated with Duke Dining leadership to provide an in-depth, quantitative accounting of the carbon footprint of the Duke Dining program. Students used the latest research quantifying CO2 equivalent greenhouse gas emissions for various food types, meals, and sources to produce calculations of the effective CO2 […]
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A team of students led by researchers in the O-Lab for auditory neuroscience determined whether the imagination of speech and nonspeech sounds can be distinguished using on a non-invasive measurement of activation in the brain, electroencephalography (EEG). Students collected and analyzed EEG data from human participants in response to both imagined and perceived sounds, as […]
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A team of students led by researchers Nicole Schramm-Sapyta, Ph.D. (Duke Institute for Brain Sciences) and Maria Tackett, Ph.D. (Department of Statistical Science) examined data from the Durham County Detention Facility and Duke Health System to analyze the effect of mental health interventions, state and local policies, insurance coverage, and other factors on the justice-involved […]
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A team of students led by professors Maurizio Forte, Classical Studies and AAHVS and Leonard White, Neurology, studied the embodied aesthetic experience engendered by real and virtual interactions with archeological ruins (“ruinscapes”) and virtual representations of places, spaces, and cultural artifacts associated with an ancient city. The focus will be the Etruscan/Roman city of Vulci […]
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A team of students led by Prof. Zuchuan Li and co-led by Prof. Nicolas Cassar developed means to estimate the amount of CO2 transferred from the ocean surface to the deep ocean through machine learning techniques applied to satellite data and automatic observations. The team identified variables that can be used to derive the estimate, […]
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A team of students collaborated with Biostatistics & Bioinformatics Professor Ethan Fang, and Fuqua Professor Yehua Wei to develop new algorithms for hospital scheduling. Optimal hospital scheduling will fully utilize the resource of the hospital and reduce the wait time of the patients. The new algorithm will lead to an automated scheduling system that could […]
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A team of students led by researchers in the Hydroclimatological Lab created a workflow/pipeline for comprehensively estimating the carbon emissions from the Southeastern (SE) United States (US) wetlands using machine learning techniques applied to multi-source data, including field measurements, remote sensing products, and biophysical model outputs. The team first applied machine learning (ML) techniques to […]
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A team of students led by an interdisciplinary group of faculty at the intersection of policy, economics, and computer science used statistical and machine learning methods to assess and map the discourse of speeches (1919-2022) by applied economists on food and agricultural policy. Understanding the historical underpinnings of these policies and the political developments can […]
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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 World, the African Continent, and […]
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This project looks at policy implications of the vast expansion of Brazil’s higher education system from 2004 to 2016, which sought to promote economic mobility and reduce social disparities. Through public and private university expansion, it doubled the number of Brazilians attending college. The project team analyzed 14 years of higher education and high school […]
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Students collaborated with the research team of Dr. Kathleen Cooney, including prominent partners both at Duke and other institutes, to identify genetic variants likely associated with early onset prostate cancer in African American patients identified by the Metropolitan Detroit Cancer Surveillance System (MDCSS) cancer registry. Students analyzed whole exome sequencing data from samples collected from […]
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A team of students led by Dr. Otis Jennings explored and created a research paper that seeks to understand the gap between the stated diversity funding, goals and support by corporations relative to their actual level of support to diverse founders. The research required navigating corporate reporting, media, press releases, etc. and will ultimately become […]
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A team of students led by Dr. Jim Heffernan of the Nicholas School of the Environment, used remote imagery and object identification tools to determine changes in parking lot occupancy in the Research Triangle region during the Covid-19 pandemic’s acute and post-acute phases. Students used open-source geospatial data to select parking lots with a range […]
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Students collaborated with CEE Professors David Carlson and Mike Bergin to model the effects of land use on the urban heat island effect using satellite imagery and ground-level temperature measurements. Students used machine learning to segment satellite images of Durham, North Carolina by land use. They then paired land use classifications with ground-level temperature measurements […]
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A team of students led by a data scientist at NetApp developed the means to evaluate technical documentation through machine learning techniques. Students identified features of language and documents that can be used to demonstrate how effective that documentation is at communicating technical specifications. Additionally, students applied machine learning methods across a large corpus of […]
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Using digitized card catalogs from the David M. Rubenstein Rare Book and Manuscript Library, a team of students explored extracting structured data from over 115,000 subject cards to develop searchable and sortable descriptions of manuscript and archival collections. They prepared the digitized subject cards for online access in the Internet Archive, and then used textual […]
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A team of students led by researchers in the BIG IDEAs Lab optimized and further developed an existing cloud-based infection detection platform that populates and translated wearable data from a variety of sources. The project involved working with existing wearable data pipelines (e.g., APIs) to collect, process, and visualize wearable device data in real-time as […]
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A team of students led by researchers from the Nicholas School of the Environment developed an app to estimate the distance of animals from a camera trap. Students merged camera trap data with terrestrial lidar (3D imagery of forest around the camera trap) to locate the position of animals. By calculating the distance between an […]
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