Project Mentor

Dr. Emil Coman
UConn Health Disparities Institute (HDI)

Undergraduate Research Opportunity Description

Project Description	The project will engage an UG student in a mutual learning process of combining methods, tools, and substantive knowledge from fields like (bio)statistics and causal inference to answer questions like: (i). (Is there, and) How strong is the causal effect of (gaining/excess) body weight/mass on diabetes/blood glucose? (ii). Does the body weight->diabetes effect differ by race/ethnicity? (iii). Are there differential diabetes prevention benefits of weight loss among different racial/ethnic groups? It proposes to share the faculty’s new insights into training methods for applied statistics/data analysis, recast in a way such that causality is imbued into the analytical process from the very beginning. It will utilize an existing dataset from a prior randomized intervention, along with other datasets the faculty has access to, and public data (e.g. ‘geo-spatial’). It proposes to introduce the UG student to new approaches to data science/analytics, using applied investigative work instead of lecturing-type expositions.
Project Direction	The ‘causal investigations in health and health disparities’ fits into the broader context of developing complex causal explanations for health and well-being disruptions. Understanding which factors (and how they) push each other up/down, and how actionable evidence can be extracted to improve the health and reduce health disparities, require cross-disciplinary reasoning using both various methodological approaches (how to ‘extract evidence’ in clinical medicine, public health, social sciences, statistics, economics) and different data sources. With so many data hubs now making patient-level data available to anyone (The All of Us https://allofus.nih.gov/ , UK Biobank https://www.ukbiobank.ac.uk/ ), along with means to connect such data to geographic/spatial, and other kinds of data, answering pointed questions like the ‘diabetes prevention benefits of weight loss’ needs a mix of solutions, from different fields, and using various technical tools (new ones becoming available as we write), like Onyx https://onyx-sem.com/ , and Jamovi https://www.jamovi.org/ , but also the rapidly rolling ‘self-learning’ new artificial intelligence tools. This proposal will follow a substantive inquiry motivated by existing data (faculty owns a dataset from a Diabetes Prevention Program randomized trial e.g.), will be guided by modern advances in the understanding of the diabetes development and prevention field, and will adjust to consider new methodological developments.
Mentorship and Supervision	End of week weekly progress monitoring meetings will be held. Troubleshooting sessions will he held, and plans for next stage work will be developed in common agreement, and co-learning goalposts will be assigned for each upcoming week. Adjustments to schedule and frequency of meetings and of output generation will be made as needed by changes in progress pace. Success, areas for improvement, and plans for next steps will be directly marked and recorded in meeting notes, and saved in a progress report timeline.
Student Qualifications	Some interest in numbers and investigating data, shown either as courses taken or self-taught, is desired. Intellectual curiosity, interest and the passion for research focused on expanding health equity among the underserved and less fortunate, and willingness to acquire practical statistical skills using underutilized rich perspectives like path analysis, geometric perspectives of statistics, and causal inference methods.
Summer Schedule Options	The UG student can work largely remotely, with daily checks over Zoom/Webex/Teams/Blackboard platforms. When work in common becomes needed, or when attending on or near-campus learning opportunities, the student will be asked to co-attend (off-campus online offers can be handled remotely). When work in person is needed, it will happen at the student’s preferred near-by location (Storrs UConn library, another UConn library, or similar public venue, or HDI offices in Hartford, e.g.).
Project Continuation	Fall 2024
Academic Year Time Commitment	3-6 hours/week
Possible Thesis Project	Yes

Application

Submit an online application for this research opportunity at https://quest.uconn.edu/prog/HRP24-7. The application deadline is Monday, January 29, 2024.

This application requires a Cover Letter, Resume or CV, Statement of Qualifications, and Statement of Career Interests. References should be available upon request.