Investigating academic hiring patterns across American universities, as well as analyzing the educational background of faculty. Worked closely with Academic Analytics, a provider of data and solutions for universities in the U.S. and the U.K.
Below is an example of the kinds of projects at Duke that have used innovative data-driven approaches to explore interdisciplinary topics. With these projects, students learn how to marshal, analyze, and visualize data, while gaining broad exposure to data science concepts, methods, and tools. The MIDS program encourages students to participate in small projects like these to hone their domain knowledge and technical expertise.
Mapping the ocean floor autonomously with high resolution and high efficiency. Efforts were part of a team taking part in the Shell Ocean Discovery XPRIZE, and they made extensive use of simulation software built from Bellhop, an open-source program distributed by HLS Research.
Analyzing potential drug diversion in the Duke Medical Center so that health care providers in helping patients recover from their condition, as well as mitigate the effects on any patients under their care.
Prototyping small-area mapping of public-health information within the Durham Neighborhood Compass, with a focus on mortality data.
Aanalyzing the spati-temporal distribution of birth addresses in North Carolina with the goal to understand how/whether the distributions of different demographic categories (white/black, married/unmarried, etc.) differed, and how these differences connected to a variety of socioeconomic indicators.
Exploring how Internet of Things (IoT) data could be used to understand potential online financial behavior. They worked closely with analytical and strategic personnel from TD Bank, who provided them with a massive dataset compiled by Epsilon, a global company that specializes in data-driven marketing.
Analyzing current and potential scholarly collaborations within the community of Duke faculty.
Analyzing data from social networks for communities of people facing chronic conditions. The social network data, provided by MyHealth Teams, contained information shared by community members about their diagnoses, symptoms, co-morbidities, treatments, and details about each treatment.