About Me
I am a fourth year PhD candidate in Statistics at the University of Michigan, supervised by 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 designing statistical approaches for conducting causal inference in observational studies where the treatment or outcome is textual, and understanding in-context learning from unstructured training data. I have also worked on estimating treatment effects in score-explained heterogeneous treatment effect models with Debarghya Mukherjee, Moulinath Banerjee, Ya’acov Ritov.
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.