Teaching Experience
I am passionate about education and have experience supporting student learning through teaching assistance.
Materials
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Teaching Assistant
Memory
Rice University | Houston, TX | Undergraduate Level
Assisted with a course on how psychologists study and understand memory and forgetting, from historical approaches to current debates—covering types of memory, neural organization, causes of forgetting, and memory accuracy. Responsibilities included leading discussion sections, grading assignments, holding office hours, and providing one-on-one support for concepts in cognitive and neuroscience of memory.
Advanced Psychological Statistics
Rice University | Houston, TX | Graduate Level
Assisted with advanced statistical methods course for graduate students. Responsibilities included leading discussion sections, grading assignments, holding office hours, and providing one-on-one student support for complex statistical concepts and methodologies.
Computational Linear Algebra
University of Minnesota-Twin Cities | Minneapolis, MN | Undergraduate Level
Supported students in computational approaches to linear algebra. Assisted with lab sessions, graded programming assignments, and provided academic support during office hours for both theoretical concepts and practical implementation.
Guest Lectures
Advanced Psychological Statistics
Rice University | Houston, TX | Graduate Level
Delivered a comprehensive guest lecture on Analysis of Variance (ANOVA) for graduate students in Advanced Psychological Statistics. Covered one-way and factorial ANOVA designs, assumptions testing, post-hoc comparisons, and practical implementation using R, with hands-on exercises demonstrating how ANOVA techniques can effectively analyze group differences in psychological research.
Computational Linear Algebra
University of Minnesota-Twin Cities | Minneapolis, MN | Undergraduate Level
Led an interactive workshop on advanced matrix decomposition methods for Computational Linear Algebra students. Covered eigenvalue decomposition, singular value decomposition (SVD), and their applications in machine learning and data science. Students implemented algorithms in Python and MATLAB, exploring practical applications in dimensionality reduction, image compression, and principal component analysis.