About me

I am a machine learning engineer at Chewy. I obtained my Ph.D. in computational data science from WashU. My research interests are computational social science, applied machine learning, causal inference and quantitative methods.

You can find my Resume and CV here.

News

• [New] Aug, 2025: I joined Chewy as a machine learning engineer.
• [New] May, 2025: I successfully defended my dissertation thesis.
• Feb, 2025: I gave a talk “Personalized Personality Modeling with Gaussian Processes: Insights from a New Longitudinal Dataset” at “Data Science for Mental Health at Alan Turing Institute”.
• Sept, 2024: “Idiographic Personality Gaussian Process for Psychological Assessment” was accepted to Neurips 2024.
• Sept, 2023: “A Gaussian Process Framework for Social Science Models” appeared on Better Models, Better Predictions panel in APSA 2023.
• AUG, 2023: “A Dynamic, Ordinal Gaussian Process Item Response Theoretic Model” appeared on Political Scaling panel in the Annual Meeting of APSA 2023.
• Jul, 2023: “Generalized budget-constrained conjoint analysis via active learning” was presented at PolMeth XL as a poster.
• May, 2023: I presented “Active learning for marginal effect estimation in Gaussian Process preference learning” at the 9th Information and Statistics for Nuclear Experiment and Theory workshop as a poster.
• Apr, 2023: I successfully proposed my Ph.D. dissertation, Advancing Modeling and Inference in Political Science with Gaussian Processes.
• Jan, 2023: “A Multi-Task Gaussian Process Model for Inferring Time-Varying Treatment Effects in Panel Data” was accepted to AISTATS 2023.
• Jul, 2022: A poster was accepted in the annual Summer Meeting of Society for Political Methodology (2022).
• Jan 2021: “Polls, Context, and Time: A Dynamic Hierarchical Bayesian Forecasting Model for US Senate Elections” was published in Political Analysis.
• Aug 2020: “Compressive Big Data Analytics: An ensemble meta-algorithm for high-dimensional multisource datasets” was published in PLOS ONE.