DEPARTMENT OF STATISTICS AND DATA SCIENCE

Teaching Philosophy

My teaching philosophy centers on cultivating students into critical thinkers who can connect statistical theory with real-world impact. I strive to bridge rigorous concepts in statistics and data science with practical applications across disciplines, helping students see the relevance of what they are learning. I believe that effective teaching begins not with what the material is, but with why it matters—framing new topics through motivating questions or real-world problems. I prioritize dialogue over monologue, creating space for active conversation and reflection. This not only deepens understanding but also fosters an inclusive and supportive learning environment where students feel comfortable engaging with the material and seeking help tailored to their individual needs.

Research Interests

My current research focuses on developing interpretable machine learning methods to uncover structure in complex systems. I am particularly interested in symbolic regression for discovering concise, human-interpretable equations that explain patterns in data. Owing to its NP-hard nature, I am leveraging statistical techniques, such as nonparametric variable selection, to improve the efficiency and scalability of symbolic regression.