Jay Park

Jay Park

Graduate Student

UIUC Profile

Bio

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.

Interests
  • Addiction
  • Social Contexts
  • Machine Learning/mHealth
Education
  • 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

Skills

Quantitative
Python

Machine Learning

R & SPSS

Statistical Analyses

Praat

Voice Analytics

Qualitative
Qualtrics

Surveys

ATLAS.ti

Mixed-Methods

NVivo

Text Processing

Experience (Abbr.)

 
 
 
 
 
Alcohol Research Lab
Graduate Research Assitant
Alcohol Research Lab
August 2023 – Present Champaign, USA
  • Alcohol Addiction
  • Social Contexts
  • Machine Learning
 
 
 
 
 
Accessible Healthcare Lab
Graduate Research Assitant
Accessible Healthcare Lab
May 2024 – August 2024 Champaign, USA
  • mHealth
  • Health Behaviors
  • Artificial Intelligence
 
 
 
 
 
LAS Administration
Graduate Research Assitant
LAS Administration
November 2023 – May 2024 Champaign, USA
  • Natural Language Processing
  • Big-Data Analysis
  • Thematic Analysis

Research Projects

*
How psychological factors influence health-promoting behaviors?
Self-efficacy and depressive symptoms are explored.
How psychological factors influence health-promoting behaviors?
Does our speech pattern change when intoxicated?
Ongoing project as a lead author since January, 2024.
Does our speech pattern change when intoxicated?
Can we detect intoxication using voice?
Ongoing project as a lead author since October, 2023.
Can we detect intoxication using voice?

Publications

The application of appreciative inquiry (AI) and its effectiveness on social welfare coursework: a case study
The application of appreciative inquiry (AI) and its effectiveness on social welfare coursework: a case study

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..

Contact

Jay is open to collaborations! Don’t hesitate to reach out via university email 🙂