Computational Psychology Research Project
Explore adolescent mental health through computational psychology research, using real-world data, statistical analysis, and machine learning to investigate factors that shape psychological well-being.
Grades 9–12
| Course Period: Fall 2026 |
| Session: October - December |
Program Structure
Grades: G9–G12
Dates: 10/5/2026 – 12/11/2026
Program Time: Mondays and Wednesdays, 7:00 - 9:00 PM PST (ONLINE)
Independent Study Hours: 4-6 independent hours per week outside the group session
Our research group will investigate factors associated with adolescent mental health and psychological well-being using computational methods and publicly available data. Adolescence is an important developmental period during which sleep, physical activity, social relationships, academic stress, screen use, and other behavioral and environmental factors may be associated with mental health outcomes.
Students will learn how to formulate research questions, review scientific literature, analyze real-world datasets, apply statistical and introductory machine-learning methods, visualize results, and interpret findings from a psychological perspective.
Prerequisites:
Students are not expected to have prior experience with advanced machine learning, programming with Python, or statistical analysis. More experienced students may work with regression and introductory machine-learning methods.
Instructor Bio
Ms. Alice has a Master of Engineering degree in Bioengineering at UC Berkeley, a Bachelor of Science degree in Bioinformatics and a minor in Cognitive Science at UC San Diego. She has several years of research experience in computational biology, using computational tools to study genomic data and human diseases.