2022 Projects
A team of students led by researchers at the Duke Marine Lab will explore the changing distribution of krill around the Antarctic Peninsula. Krill are a key prey species in this ecosystem, supporting a number of animals including whales, seals, and penguins, but they are dependent on winter sea ice...
Today we design communication networks using mathematical models that describe components of the system that affect end-to-end performance. As wireless links become more highly variable, and system components become harder to model, this approach is losing ground. A team of students led by Dr. Robert Calderbank, Dr. Christ Richmond, Dr. Lingjia Liu,...
This project is also part of Duke’s first Climate+ cohort. A team of students led by researchers at Duke and abroad will develop and evaluate machine learning solutions to model behavioral patterns of electric use, emphasizing data privacy. Data collected in different parts of the world will be analyzed to...
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 Professor Anru Zhang (Duke Biostatistics & Bioinformatics, Computer Science, Mathematics, and Statistical Science) will develop methods to investigate the courses of complex diseases through electronic health records. The team will apply tensor methods to identify key features to register the patient’s timeline. This work...
A team of researchers associated with the Applied Machine Learning Lab in Duke’s ECE department 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...
Data+ students led by Prof. Henri Gavin will develop AI models for on-site earthquake early warning, in which sensors at a site provide warnings at that site. The Data+ project will integrate into ongoing work on geophone sensors, IOT microcontrollers, and networking. The Data+ team will focus on machine learning...
This project is also part of Duke’s first Climate+ cohort. A student team working with the Energy Data Analytics Lab will work to democratize access to data relevant to climate change mitigation and adaptation planning as well as the underlying models to acquire those data. This project will work towards building the...
A team of students led by Courtnea Rainey, David Jamieson-Drake, and Edward Balleisen will explore survey data from completers of the PhD and Duke PhD alumni to establish important correlations, document key patterns and longitudinal trends, and develop visualizations that can inform institutional decision-making. In addition to updating the work...
A team of students led by researchers in the BIG IDEAs Lab will work to create a cloud-based infection detection platform that populates and translates wearable data from a variety of sources. The project will involve working with existing wearable data pipelines (e.g., APIs) to collect, process, and visualize wearable...
This project is also part of Duke’s first Climate+ cohort. A team of students led by researchers in the Hydroclimatological Lab will comprehensively quantify the wetland carbon emissions in the entire Southeast (SE) US using machine learning techniques and various climate datasets—including in situ measurements, remote sensing data, climate observations,...
A team of students led by Co-Principal Investigators Dr Jenny Immich and Dr Vicky McAlister will develop a geospatial methodology to automate data analysis originating from small unmanned aerial vehicles (SUAV) that seeks to identify the homes of ordinary medieval people within the modern Irish landscape. Known as aerial archeology,...
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 and...
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...
This project is also part of Duke’s first Climate+ cohort. Duke Data+ students, in collaboration with Dr. Emily Bernhardt (faculty advisor) and Audrey Thellman (graduate student) will evaluate how changing ice and snow conditions are impacting river ecosystems through classified ice imagery. Currently, our team has data from 7 field...
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...
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