| Affiliations: | |
| Project Leader: | Irina Gaynanova irinag@tamu.edu Statistics |
| Faculty Mentor: | Dr. Irina Gaynanova, Ph.D. |
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Meeting Times:
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TBD |
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Team Size:
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4
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| Open Spots: | 0 |
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Special Opportunities:
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– earn STAT 491 credit; – flexible schedule besides required meeting time; – completely remote work; – opportunity to learn R and R package development; -potential of co-authorship on developed software products and resulting publications
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Team Needs:
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Must: Knowledge of R programming language, willingness to learn on the spot, willingness to read literature on diabetes and wearable devices as needed. Big plus/priority will be given to candidates who also have some of the following: experience with git and GitHub , ggplot2 in R, Shiny apps using R, previous experience with diabetes research |
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Description:
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Data from wearable devices, such as continuous glucose monitors (CGM), activity trackers, ambulatory blood pressure monitors, sleep EEG monitors, are increasingly common. These wealth of data has the potential to improve the health outcomes and improve management of various chronic diseases, e.g. diabetes, however often only the crude summary statistics (e.g. mean of measurements) are used in subsequent analyses. The purpose of this project is to develop software and statistical methods to facilitate analyses of such data in the context of diabetes research, as well as to perform explorative analysis of possible relationships between data from different wearable monitors. This is a long-term project, with the specific directions each semester driven by the previous findings and team developments. Some specific tasks that students will be expected to do as part of the team: coding and testing existing code in R (possibly python), contributing functionality to existing R packages, data processing (cleaning, reformatting), data visualization (in R primarily), shiny web app development, literature review, statistical analyses of wearables data, development of new algorithms for processing/analyses |