About Me

I am a third year PhD candidate in Statistics at the University of Michigan, supervised by Prof Yixin Wang. My research aims to bridge the gap between the theory and practice of machine learning algorithms, synchronize model assumptions with real-world contexts, and develop statistical methods in areas where the application of statistical machine learning has been suboptimal.

I currently work on understanding in-context learning from unstructured training data. Additionally, my work involves designing statistical approaches for conducting causal inference in observational studies where the treatment or outcome is textual. I have also worked with Prof Debarghya Mukherjee, Prof Moulinath Banerjee, and Prof Ya’acov Ritov on estimating heterogeneous treatment effects in regression discontinuity designs.

I graduated from National University of Singapore (2019) with a BS (Honours) in Applied Mathematics and Statistics, and Columbia University (2020) with an MS in Data Science. Prior to joining the PhD program, I gained valuable experience in data analysis and data science through roles at various companies, including Walmart and Traveloka.

I am very passionate about teaching, and I always strive to get better at communicating difficult concepts to my students. Besides teaching, my interests include watching badminton matches, playing the piano and guitar, indulging in bossa nova and Korean indie music, and exploring the world through the game of Geoguessr.

Here are my CV and resume.