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. Build data-centric evaluation and improvement loops for AI models, agents, and auto-research workflows, combining expert feedback, adaptive experimentation, and production systems.

2021-2026: Adaptive Experimentation, Central Applied Science. Developed Bayesian optimization methods for large-scale AI and engineering systems, with a focus on human-aligned, sample-efficient learning from preferences and other scarce feedback under expensive, multi-objective, or feedback-limited settings; core contributor to open-source BoTorch and Ax.

2019-2020: Research Intern/Collaborator on preference learning, multi-objective decision modeling, and Bayesian optimization for online experiments.

Built ML models for user behavior and article-forwarding prediction using latent overlapping group structure; improved baseline performance by 20%.

Developed ML-based marketplace recommendation models for related and complementary products; improved an internal related-product recommendation baseline by 30%.

Improved Yahoo Homepage mobile article-end experience, increasing engagement time for a target user group by 3x.