About

I am a research scientist at Meta on the AI & Systems Co-Design team.

I received my PhD in Computer Science from the Paul G. Allen School of Computer Science & Engineering at the University of Washington, where I was very fortunate to be advised by Magdalena Balazinska.

My PhD research focuses on the intersection of data management and AI. Data-driven AI models present significant challenges for data systems because they learn from and generate large volumes of data (artifacts) that traditional data systems are not designed to handle. At the same time, the immense computational and memory demand of AI models has driven the development of specialized AI infrastructure, offering new opportunities for accelerating not only AI workloads but also database queries. My research aims to bring the two worlds together by designing novel systems for data-intensive AI workloads (e.g., NeuralArtifactDB) and incorporating AI infrastructure into data management systems (e.g., Tensor Query Processing and its demo which received the Best Demo Award at VLDB’22). My dissertation is titled Data Systems for Explainable AI and Incorporating AI Infrastructure into Data Systems.

During my PhD, I interned at Microsoft Gray Systems Lab and Snowflake, and have been collaborating / collaborated closely with Ranjay Krishna, Matteo Interlandi, Jiaqi Yan, and Alex Ratner. Before UW, I obtained my B.S. (Honors) in Computer Science at Fudan University. I visited Peking University in 2017. In my younger years, I was a contestant in ACM-ICPC and Olympiad in Informatics.

My reasonably up-to-date CV is available here.