April 8, 2026 Audrey Cheng — UC Berkeley
Data systems face increasing performance challenges as modern applications, particularly AI workloads, evolve rapidly. In this talk, I discuss how we can address these challenges. First, I will present my work on traditional database optimization methods, focusing on data contention, which remains a crucial performance bottleneck. My research addresses this challenge by revisiting transaction scheduling: instead of resolving conflicts after they occur, I focus on preventing them by intelligently reordering transactions before execution. I will then discuss how we build on these results by leveraging AI-driven methods to enable the rapid exploration and generation of optimization strategies. This direction advances the broader vision of automating performance optimization in data systems.
Audrey is a PhD student at UC Berkeley, advised by Natacha Crooks and Ion Stoica. Her research focuses on performance optimization for database systems. Her work has been deployed in industry databases at Meta, PlanetScale, and TiDB. She was named a Rising Star in EECS and has received an NSF GRFP Fellowship, a Meta Research PhD Fellowship, a Berkeley Chancellor’s Fellowship, and a VLDB Best Industry Paper Award.