2026 Summer Research Internship Program

This study uses a learning health system approach to improve care delivery and outcomes for cancer survivors in a large academic community health system. By integrating electronic health record data with patient and clinician input, the project will assess guideline-concordant survivorship care, quality, utilization, and patient-centered outcomes to inform data-driven implementation strategies. Applicants should have experience in healthcare data analysis, qualitative or mixed-methods research, implementation science, and/or health policy. Roles and responsibilities will be tailored to the applicant’s expertise.

Investigating how neighborhood and psychosocial stressors affect cognitive health. The intern will be involved in survey data analyses, literature review, and manuscript writing.

The intern will support a research project using national datasets (a cohort study linked to Medicare claims and American Community Survey data) to examine disparities in dementia diagnosis and care. Responsibilities include data cleaning, construction of analytic datasets, generation of descriptive tables and visual summaries, and support for statistical analyses. This project is well-suited for trainees interested in biostatistics, health services research, and population health, particularly those seeking to strengthen programming skills (e.g., SAS for Medicare claims). Responsibilities will be tailored to the trainee’s skills and interests. (Ability to come in person preferred)

Obesity & Diabetes Epidemiology – Secondary Data Analyses: The intern will work with an interdisciplinary team of faculty, medical residents, research assistants, and analysts to analyze existing cohort and clinical trial data (e.g., ARIC, CARDIA, All of Us, ACCORD, SPRINT) to evaluate the prognostic value of the 2025 Lancet definition of obesity and identify metabolic clusters among adults with obesity. The intern will gain hands-on experience in observational research methods, causal inference, and unsupervised machine learning. The project will culminate in the development of a conference abstract for ObesityWeek or the American Diabetes Association, with the goal of producing a peer-reviewed manuscript.

Health Equity-focused Youth Substance Use Research: The intern will contribute to a secondary data analysis project addressing cultural and environmental influences on substance use in Black and Latinx youth that draws data from a large-scale longitudinal study of early adolescents. In addition to supporting manuscript development, a student with a strong statistical background may take lead on analyses.

My research examines adaptive intervention designs and their application to the field of mHealth. I am specifically interested in how research designs (e.g. factorial designs, SMART, and microrandomized trials) can be leveraged to optimize interventions prior to efficacy testing in a standard RCT. The most recent study tailored an app based just-in-time adaptive intervention to reduce stress and substance use cravings in sexual minority men living with HIV.

Generating Life-Course Inequality: Cross-National Analysis of Late-Life Economic Inequality and Its Relationship to National Retirement Income Policies. This project will utilize data from the Organization of Economic Cooperation and Development (OECD)’s Income Inequality and Wealth Inequality databases, along with cross-national policy databases, to examine national patterns of late-life inequality, relative to inequality at earlier ages, in the US and across OECD countries.

This work will be informed by the cumulative advantage/cumulative disadvantage (CAD) model widely used in gerontology to examine the pathways and extent to which social policies can exacerbate, maintain or offset the cumulative effects of early advantages and disadvantages over the lifecourse on late-life inequality. Preliminary analyses of OECD show that retirement income institutions in the US, closely tied to pre-retirement income, perpetuate the effects of CAD processes leading to higher late-life than earlier-life inequality, while differently-structured retirement income systems in some other OECD countries are associated with reduced late-life inequality.

The student would work with faculty on a paper developing this line of inquiry further utilizing publicly available cross-national data. There would be many opportunities for the student to develop this line of inquiry along lines of interest, such as in-depth analysis of late-life inequality in particular countries of interest, analysis of historical trends, or extension to health or other outcomes using other publicly available comparative datasets.

National, State and Community-Level Changes in Buprenorphine Prescribing for Opioid Use Disorder: This project will utilize all-payer national pharmacy data (IQVIA) that capture >92% of prescriptions filled in the U.S., for a paper that analyzes state level buprenorphine treatment rates and trends. Buprenorphine is the most widely-used form of MOUD and can be prescribed in an office-based or virtual manner, without the burdensome daily in-person attendance required for methadone. It is a key tool for reducing fatal-overdose rates. A previous analysis (published in Health Affairs in September 2025) shows that access and use vary greatly across states, lagging especially in states that have not expanded Medicaid, but much more work needs to be done on relationship of trends to state policy actions and in relation to factors such as fatal-overdose rates, survey-based estimates of OUD prevalence, Medicaid expansion, state implementation of Medicaid reauthorization processes, trends by prescriber type and payer, and other factors. In addition, variation at substate levels (e.g. county, city) needs to be studied.

The student will have the opportunity to work with the research team on analysis of recently updated national data and to propose additional questions to be explored in this important dataset.

The intern will work on a variety of projects intersecting neuropsychology and digital health, such as predicting fatigue severity from a wearable sensor and predicting dementia status from digital voice biomarkers. Proficiency in Python is required, and knowledge and experience with signal processing, machine learning, and natural language processing are preferred.

Precision medicine is an appealing concept. Several core challenges still impede translation from bench to the bedside, including heterogeneity challenges across varied data sources, integration challenges facing privacy and permission concerns, and real-world challenges with a wider and more complicated disease range than training sets. The research objectives of this project include understanding the impact of translational research and precision medicine. Furthermore, investigating Artificial Intelligence (AI) and Machine Learning (ML) approaches using multimodal data for the novel biomarker discovery and predictive medicine.

Applicant Responsibility: The selected candidate will be expected to have good interest and motivation to participate in this important research project. It requires reading and understanding state of science literature and implementing innovative ideas. Furthermore, it’s important to attend lab/project meetings (e.g., Monday, Wednesday, Friday), timely report/present progress, collaboration with fellow lab members, and document findings.