Jay is a graduate student in Clinical-Community Psychology at the University of Illinois, Urbana-Champaign. Broadly, he is interested in mental health and applied methodologies for real-world impact. Specifically, his research focuses on addiction problems through a combination of in-lab and mobile methods.
Before graduate school, Jay gained diverse work experience as a road cyclist, guitarist, and military counterintelligence agent. In his free time, he enjoys road cycling, going on road trips, and taking pictures.
M.S. in Psychological Science (GPA 4.0/4.0), 2025 (Expected)
University of Illinois, Urbana-Champaign
B.S. in Psychology (GPA 4.0/4.0), 2019
University of Utah
Machine Learning
Statistical Analyses
Voice Analytics
Surveys
Mixed-Methods
Text Processing
The primary goal of the present study is to examine how alcohol consumption affects vocal traits, such as volume, pitch, jitter, shimmer, and socio-emotional rewards. The secondary objective is to detect intoxication from soberness based on the aforementioned acoustic features. We seek to bridge the knowledge gap in this research area and advance our comprehension of how alcohol influences speech features, possibly indicating alcohol’s rewarding effects and inebriation from a socio-contextual perspective.
Word frequency and TF-IDF (Importance) revealed that school violence was the key suicidal factor in adolescents. The surrounding causes were parents, smartphone addiction, school policies, etc., indicating indirect influences.
Objectives The purpose of this study is to identify the effectiveness of appreciative inquiry (AI) and how it can be applied to social welfare coursework. Ultimately, the present study aims to boost participatory learning, discover positive aspects of students, and enhance the capability of voluntary AI learning. Methods D University students (N = 34) participated in the 5-D model AI learning. Pre- and post-test was completed to evaluate the effectiveness of AI. Results The 5-D model (e.g., Define, Discovery, Dream, Design, and Desitny) with a single theme for each group appears to be the most desirable method for employing AI in social welfare coursework. Pre- and post-test reveals that the AI class effectiveness of individuals, teams, and team cooperation is augmented. Specifically, the individuals’ positive psychological capital varaibles illustrate the highest (average .53 higher). Conclusions Results suggest that AI application in social welfare coursework may be effective for participatory learning and voluntary AI learning. To better implement AI in coursework, future research should consider developing systematic AI processes and objective evaluation tools based on the 5-D model, as well as exploring an action plan, a best practice, and positive questions corresponding to social welfare courses..
Jay is open to collaborations! Don’t hesitate to reach out via university email 🙂