Stanford, California, United States
I am a research scientist in Meta's Applied AI organization, where I work on data-centric methods for evaluating and improving AI models, agents, and auto-research workflows through expert feedback, human-in-the-loop optimization, and data-efficient learning. Previously, I was on the Adaptive Experimentation team in Meta's Central Applied Science, where I developed Bayesian optimization and adaptive experimentation methods for turning human feedback, preferences, and behavioral signals into reliable evaluation and optimization signals for AI platforms and other large-scale systems.
2026-Present: Applied AI. Develop data-centric methods for evaluating and improving AI models, agents, and auto-research workflows through expert feedback, human-in-the-loop optimization, and data-efficient learning. 2021-2026: Adaptive Experimentation, Central Applied Science. Developed Bayesian optimization methods for large-scale systems, with a focus on preference learning and human-guided optimization under expensive, multi-objective, or feedback-limited settings. Applications span AI platforms, recommender systems, online product experimentation, infrastructure, hardware design, and simulation-based engineering/science problems. June 2019-September 2020: Research Intern and Collaborator with the Adaptive Experimentation team on preference learning, multi-objective decision modeling, and Bayesian optimization for online experiments.
Research Intern at Wechat Data Center working on user behavior modeling and article forwarding prediction.
Worked on complementary product recommendation at eBay NYC discovery team.
Have been researching with Georgia Tech Professor Polo Chau in areas including Visualization, HCI and Data Mining since spring 2013. Also have ongoing research projects with Georgia Tech Professor Munmun De Choudhury and Professor Eric Gilbert about Social Computing respectively.
Interned at eBay Marketplace in New York City. As part of discovery team, Leveraged data science knowledge and machine learning techniques to improve eBay's recommender system. Collected data and build models both locally and scaled up on Hadoop cluster.