GREAT Faculty Mentors

The Graduate Research Experiences and Applied Training (GREAT) in Alcohol Studies program through the Rutgers Addiction Research Center offers undergraduate students the opportunity to engage in research experiences within participating Rutgers faculty labs. Please find a list of participating Principal Investigators (PIs) and descriptions of their research and opportunities within their labs below.

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My research focuses on determining biological mechanisms that contribute to the expression and development of externalizing behavior in adolescence. I am particularly interested in understanding the biological, environmental, and developmental influences on the emergence, persistence, and desistance of antisocial behavior and substance use in adolescence and early adulthood. Towards this end, I incorporate a broad range of methods to better understand this phenotypic expression. I am also trained as a clinical psychologist and am interested in translating these mechanistic findings into clinically relevant measures or interventions for at-risk youth. Students will contribute to data collection, data cleaning, basic data analysis, and literature reviews.

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Research projects use digital health methods (e.g., data collected using smartphone, wearable device like Fitibit) to understand how “in the moment” feelings and behaviors captured by daily phone surveys and wearables (e.g., activity level, sleep) can be used to predict and better manage mental and physical health. A student has the opportunity to be involved in literature review; after training and with on-going supervision: participant recruitment (e.g., describing the study to people who express initial interest in the project prior to consent), updating screening and recruitment numbers using R-script, assisting with data collection (e.g., monitoring participant completion of daily data), participating in preliminary analyses (depending on skill level in data analyses). Student with interest and experience / familiarity with smartphones (Android, iOS), apps, and wearables preferred.

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My research explores how digital technology tools can be used to improve assessment and treatment for substance use problems and co-occurring mental health distress. Ongoing projects include 1) a randomized clinical trial examining a data-driven approach to counseling for co-occurring anxiety, traumatic stress, and problematic drinking, 2) an observational study examining daily processes that facilitate treatment and recovery in a residential treatment center, and 3) meta-science projects examining the utility of experience sampling methods for clinical research and practice. Students will contribute to data cleaning and analysis, survey/materials design and programming, qualitative interviewing and coding, and literature review and extraction.

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My lab employs frameworks of behavioral economics and applied behavioral science to understand patterns of decision making in addiction. Current research in the lab involves behavioral assessments of alcohol reward and the impact of alternatives on the rewarding value of alcohol, understanding substance use patterns in neurodiverse populations, and methodological refinement of behavioral economic frameworks of substance use. Research in the lab is conducted with participants both virtually and in-person in our laboratory in Piscataway via behavioral surveys and tasks. Students will be expected to complete literature reviews, assist in data collection during in-person research studies in the lab, and analyze data where applicable. Students will have the opportunity to collaborate on posters and presentations that are developed from lab research, if interested.

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We are interested in the transdiagnostic features of psychopathology. We use a combination of experience sampling, and neuroimaging. Current studies focus on relations between the structure of psychopathology and the ability to resist temptations, and brain changes in chronic alcohol use disorder. Student tasks will range from data management and quality control, running subjects through study procedures, to data analysis, with roles depending upon student interest and existing computer skills. Students with some computer programming experience are preferred.

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