Liz Wai-Ping Ng
In any business, time is money. So it’s hardly surprising that many sectors have embraced systems powered by artificial intelligence (AI) and machine learning (ML): these tools promise to make time-consuming processes more efficient.
Zixuan Yi, a second-year doctoral student in Computer and Information Science (CIS) at the PRECISE (Penn Research In Embedded Computing and Integrated Systems Engineering) Center, is working to further improve AI and ML performance.
Yi’s research tackles a thorny problem in data management: query optimization, the task of quickly retrieving data relevant to a user — or AI agent — request.
“By bridging the gap between learning methods and real-world system constraints, I aim to create adaptive, automated solutions that continuously optimize performance and meet evolving user needs,” says Yi, who came to Penn Engineering in 2023 after graduating from Tsinghua University and relocating from Beijing.
Working with her advisors Ryan Marcus, Assistant Professor in CIS, and Zachary Ives, Adani President’s Distinguished Professor and Department Chair, Yi recently co-authored a paper introducing LimeQO, which optimizes multiple queries collectively rather than in isolation, unlocking substantial performance improvements across large workloads.
Yi will present the paper, the first to integrate both neural and linear methods in query optimization, at SIGMOD 2025, one of the world’s premier data management conferences.
“LimeQO represents a significant shift in learned query optimization,” says Marcus. “Yi’s work is especially interesting because it shows how a relatively old AI concept (i.e., low-rank matrix completion) can drastically decrease the amount of data required for training learned systems.”