Teaching
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I always try to take on teaching roles whenever I have the chance, because teaching is the best way for me to truly understand something and I genuinely enjoy helping others grasp the core ideas in the areas I care about.
When I first started teaching, I was both excited and extremely nervous, but I kept doing it because teaching forces me to refine my understanding of the entire system rather than just individual concepts. With more practice, I have gradually become much more comfortable speaking and connecting with students, and although I know I still have a long way to go, seeing this growth has been incredibly motivating. Teaching is something I truly enjoy because it allows my own development to happen alongside helping others learn, and I hope that wherever I end up, I will always stay open to roles where I can share what I have learned with others.
Harvard University
CS1090A: Introduction to Data Science
Core undergraduate course in data science covering EDA, visualization, regression, Bayesian and hierarchical models, classification, decision trees, random forests and ensemble methods including boosting. Includes a major project applying data science to real world datasets. As a Teaching Fellow, I graded assignments and quizzes, revised quiz questions, led weekly review sections, attended staff meetings, and mentored two student project teams.
The Chinese University of Hong Kong, Shenzhen (CUHKSZ)
BIM3001: Introduction to Bioinformatics
Introduces computational methods for analyzing biological data, covering sequence analysis, phylogenetics, and structural bioinformatics.
BIO4203: Immunology
BIO2002: Cell & Molecular Biology
BIO1001: General Biology
BIO1008: Life Science & Chemistry
Chemistry Pre-sessional Course
X Academy
An immersive 12-day summer program for high school students that explores cutting-edge topics through intensive instruction, hands-on labs, and a capstone research project.
Introduction to Artificial Intelligence and Data Science
Covered the full data science workflow, including data preprocessing, statistical analysis, machine learning, neural networks, deep learning, algorithmic thinking, model development, and research paper reading.
Virology, Immunology, and Bioinformatics
Introduced viral infection and transmission, immune system mechanisms, COVID-19 pathogenesis and vaccines, and foundational bioinformatics tools including biological databases, sequence alignment, and phylogenetic analysis.