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.
The project involves programming with Python and statistical analysis. More experienced students may work with regression and introductory machine-learning methods. Students are not expected to have prior experience with advanced machine learning and programming experience is helpful but not required.
Week 1 — Introduction to Adolescent Mental Health and Computational Psychology
Week 2 — Literature Review and Research Question Development
Week 3 — Python and Data Analysis Fundamentals
Week 4 — Exploratory Data Analysis and Visualization
Week 5 — Statistical Analysis
Week 6 — Multivariable Modeling
Week 7 — Introduction to Machine Learning for Psychological Research
Week 8 — Model Interpretation and Psychological Interpretation
Week 9 — Research Paper Development and Experiment Refinement
Week 10 — Research Paper Finalization and Presentation
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.
Grades: G9–G12
Dates: 10/5/2026 – 12/11/2026
Class Length: 2 hours / session
Frequency: Twice a week
Weekly Commitment: 10 weeks, Mondays. and Wednesdays at 7-9PM PST ONLINE
Tuition: $7500
