My research revolves around the inference and estimation of parameters arising in causal inference using non/semi-parametric statistical models. While I am interested in developing statistical methods motivated by asymptotic properties (e.g., consistency, efficiency), I have also studied how to sufficiently reduce the space of statistical model by leveraging semi-parametric constraints (e.g., set of conditional independence). I work on these problems to develop consistent, robust, and efficient learning frameworks for causal analysis from both observational and experimental data.
Previously, I received my B.S. in Computer Science and minor in Engineering Statistics from Cornell University in 2019, where I was advised by Thorsten Joachims.
Outside of Computer Science, I enjoy studying art history, particularly the philosophy developed by Guy Debord that our socio-cultural beliefs are governed by a photographic representation of the world instead of physical experiences. Living in the age of photographic representations, or the Spectacle, I am interested in the role of visual culture (e.g., films, architecture, typography) in defining both personal and socio-cultural concept of aesthetics.
|Jun 1, 2020||“Causal Inference using Gaussian Processes with Structured Latent Confounders” has been accepted as a long paper at the 2020 ICML conference|
|Apr 15, 2019||“A General Framework for Counterfactual Learning-to-Rank” has been accepted as a long paper at the 2019 SIGIR conference|