Computational social science, algorithm-assisted human decision making, data science, applied statistics and machine learning.

Info-internetworks and intelligence threads, with all theory thread courses completed for CS major. Probability and statistics track for math minor.

2026-Present: Applied AI. Develop methods for improving and evaluating AI systems and agents through expert feedback, human-in-the-loop optimization, and data-efficient learning.

2021-2026: Adaptive Experimentation at Central Applied Science. Bayesian optimization, AI-moderated preference learning, and experiment decision modeling methods for sample-efficient product optimization and experimentation.

Interned and collaborated externally at the Adaptive Experimentation team under Core Data Science. Worked on preference learning and experiment decision modeling.

Interned at WeChat. Worked on user behavior modeling and article forwarding prediction leveraging latent overlapping group structures; improved performance by 20% compared to baseline model.

Interned at Discovery team at eBay NYC office in summer 2015 and summer 2016. In summer 2015, I worked on eBay's related product recommender system. The resulted model outperformed eBay's existing counterpart by 30% in internal test. In summer 2016, I worked on complementary products recommender systems and devised user group separation models with mathematical and machine learning techniques.

Interned in Yahoo's Homepage & Vertical team, Worked on Yahoo's homepage, mainly the mobile website. Worked collaboratively with product managers, designers, and engineers to improve the user experience at the end of articles. My work increased a group of users' engagement time by 3 times.