Advisor

• Teaching Philosophy

I guide students to ask and answer the question: 

Why does this matter?

By helping them connect statistical and data‑driven concepts

to real‑world relevance, 

I foster deeper understanding and ownership in learning.

• Teaching Experience  

1. Undergraduate Courses

• Introduction to Statistics 

• Exploratory Data Analysis (using Python)

• Design of Experiments (within the potential outcome framework for causal inference)

• Causal Inference (within the potential outcome framework)

• Multivariate Data Analysis

• Clustering Analysis (covering both distance-based and model-based clustering)

• Data‑Driven Design Thinking (focusing on data‑informed decision-making)

2. Graduate Courses

• Advanced Statistical Inference (with latent variables)

• Advanced Design of Experiments (within the potential outcome framework for causal inference)

• Generalized Linear Models (including robust regression and cluster-weighted modeling)

• Advanced Multivariate Data Analysis

• What Students Say: