| Affiliations: | DeBakey Executive Research Leadership Program |
| Project Leader: | Irina Gaynanova, Ph.D. irinag@tamu.edu Statistics |
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Meeting Times:
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Thu 4-5:30 |
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Team Size:
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3
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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 times
– remote work beyond meeting times (with possibility of hybrid mode)
– potential of co-authorship on developed software products and resulting publications
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Team Needs:
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Must: Knowledge and experience working with R programming language, knowledge of basic statistical methods in R (e.g. linear models), willingness to learn on the spot, willingness to read medical literature and literature on statistical methods as needed.Big plus/priority will be given to candidates with: 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. 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. |