TADA Fellows

Current Trainees

Caroline Barry
–  PhD Candidate Behavioral, Social, and Health Education Sciences
–  SUD-related research interest: social support and social network normative influence in relation to substance use and mental health among adolescents in the Cherokee Nation
–  Advanced Data Method: Natural Language Processing and Machine Learning 
 
 

Snigdha Peddireddy
–  PhD Candidate Behavioral, Social, and Health Education Sciences
–  SUD-related research interest: implementation of federal and state laws that impact the health of PWUD, with a focus on Black, Indigenous, and Latine/-x PWUD
–  Advanced Data Method: Machine Learning & large administrative databases
 
 
 
 
 

Erin Rogers
–  PhD Candidate Epidemiology
–  SUD-related research interest: How SUDs influence treatment trajectories for individuals living with HIV
–  Advanced Data Method: Machine Learning & Causal Inference
 
 
 
 

Drew Walker
–  PhD Candidate Behavioral, Social, and Health Education Sciences
–  SUD-related research interest: Quality-of-care outcomes for populations frequently prescribed opioids.
–  Advanced Data Method: Machine Learning and Natural Language Processing
 
 
 
 

Simone Wien
–  PhD Candidate Epidemiology
–  SUD-related research interest: structural racism, the lived environment, and pregnant and postpartum PWUD patient-provider interactions
–  Advanced Data Method: Geospatial Information Systems & large administrative databases

 


 Past Fellows

carla_jones_harrell
Carla Jones-Harrell 
–  PhD Behavioral, Social, and Health Education Sciences
–  SUD-related research interest: Opioid-related outcomes.
–  Advanced Data Method: Geospatial Information Systems
–  Completed: Summer 2022
 
 
 
lauren_bertin
Lauren Bertin
–  Clinical Psychology
–  SUD-related research interest: Alcohol & cannabis use disorders
–  Advanced Data Method: Gene-Environment interactions
–  Completed: Summer 2022
 
 
 
Joni Webster
–  PhD Sociology – Health
–  SUD-related research interest: How Black Twitter users Discuss Mental & Emotional Wellbeing in Relation to Non-Medical Drug Use, Death, and Everyday Life
–  Advanced Data Method: Machine Learning/Natural Language Processing