Interdisciplinary Inquiry Projects
Ellis Ackerman (Math, NCSU), Rodrigo Araujo (Computer Science), and Samantha Miezio (Public Policy) spent ten weeks building tools to help understand the scope, cause, and effects of evictions in Durham County. Using evictions data recorded by the Durham County Sheriff’s Department and demographic data from the American Community Survey, the team investigated relationships between rent and...
Pryia Juarez (BME/ECE), Jonathan Pilland (ECE/BME), and Matthew Traum (CS/Econ) spent teen weeks analyzing sensor data synthesized by an agile waveform generator. The team used deep reinforcement learning techniques to understand the performance of different synthetic agents representing potential attackers to the sensor system.
A team of students collaborating with Duke School of Medicine’s Root Causes Fresh Produce Program, community members, and physicians throughout the Duke Health network will help integrate data from food deliveries to Duke Health patients with patient health record data and other available data sources to create a dashboard that...
A team of students will partner closely with the City of Durham’s newly formed Community Safety Department. The Community Safety Department’s mission is to identify, implement, and evaluate new approaches to enhance public safety that may not involve a law enforcement response or the criminal justice system. The student team...
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...
The QSIDES Institute has pulled thousands of pages of police records from the Williamstown Police Department. This project will analyze and identify patterns in police behavior in the town, work with journalists and other activists to use the data to develop action plans to address problems and enact public safety...
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...
A student team led by researchers at Duke Surgery and Global Health Institute will further develop the computer application – Alcohol Use Behavioral Phenotyping Test (AUBPT) that can help predict alcohol use and alcohol use disorder risks based on personal characteristics and behavioral performance on Research Domain Criteria paradigms/games. Students...
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...
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