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Data-driven Geospatial Analysis of Mental Health and Community Contexts in the US (Apprentice Position)
Shodor > NCSI > XSEDE EMPOWER > XSEDE EMPOWER Positions > Data-driven Geospatial Analysis of Mental Health and Community Contexts in the US (Apprentice Position)
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Status
Completed
Mentor NameXinlian Liu
Mentor's XSEDE AffiliationCampus Champion, Current Fellow
Mentor Has Been in XSEDE Community4-5 years
Project TitleData-driven Geospatial Analysis of Mental Health and Community Contexts in the US (Apprentice Position)
SummaryMental Health risk analysis and mitigation have been a challenge for public health researchers. Recently available large data sets and machine learning offers new opportunity to tackle this challenge. We have gathered a large data set with hundreds of community characteristics on county and Zipcode levels. We have also developed graph learning methods to better understand the data. In addition, we are working on visualization techniques to improve the understanding of high-dimensional data. The goal is to develop a model to delineate the environmental and social determinants in regard to suicide prevention.
Job Description1) Collect and gather data from agencies and collaborators; perform pre-processing such as cleaning, imputation, and association analysis;
2) Produce documentation;
Computational Resources1) XSEDE educational allocation;
2) We also have access to some computing resources through our collaborators
Contribution to Community1) A better understanding of the relationships between social and environment factors and mental health;
2) workforce development
3) We will disseminate our findings by technical papers
4) Coding and intermediate results will be available to the open-source community.
Position TypeApprentice
Training Plan1) Assign and read textbook chapters/tutorials with the interns;
2) Weekly meeting to discuss progress;
3) Slack and other tools to facilitate interaction;
4) Opportunity to present to professional conferences and workshops
Student Prerequisites/Conditions/Qualifications1) Experiences with Linux server 2) Advanced statistics 3) Python programming, Pandas, scikit-learn 4) Scientific reading/writing skills 5) An insatiable appetite for learning
DurationSemester
Start Date01/15/2022
End Date04/15/2022
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