Jialin holds a BS in Business Analytics from the University of Denver and an MS in Applied Data Science from the University of Chicago. Her blend of academic training and corporate experience offers a unique perspective to her research, having worked for two years in the business sector before transitioning to the academic field. This diverse background enriches her analytical approach, enabling her to integrate practical insights into her work. In her current role, Jialin specializes in applying advanced data science techniques to mental and behavioral health research. Her expertise spans machine learning, deep learning, and survival analysis, leveraging methods such as mixed-effects models, logistic and linear regression, decision trees, support vector machines, and neural networks. She works with a wide range of datasets, including passive and active sensing data, EMA, and EHR, with proficiency in Python and R. Driven by a passion for improving mental health outcomes, she has successfully applied these techniques to enhance predictive modeling in mental health assessments. Jialin plans to continue developing her expertise in mental health research, with aspirations to pursue a PhD in bioinformatics to create data-driven solutions for better mental health care.