Supawit Chockchowwat
Supawit is a currently a Visiting Faculty Researcher at Google and an incoming Assistant Professor at CMKL University, focusing on database efficiency research at scale and in emergent areas. His research interests revolve around data systems at the intersection of database management systems, data mining, probabilistic algorithms, machine learning, and distributed systems. He received his Ph.D. from the University of Illinois Urbana-Champaign (UIUC) in 2025 and B.S. from the University of Texas at Austin in 2020
Authored Publications
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QBAT: Model-based Query Budget Autotuner for Clustering-based Approximate Nearest Neighbor Search
Jonghyun Bae
Tae Jun Ham
Alan Li
Yannis Papakonstantinou
Proceedings of the VLDB Endowment (2026), pp. 3091-3104
Preview abstract
Approximate nearest neighbor search (ANNS) is a critical component in modern data-intensive applications, but its performance is often hindered by the use of a static query budget parameter. This one-size-fits-all approach, even if well-tuned, fails to account for the varying difficulty of individual queries, inevitably leading to suboptimal latency on easy queries and poor accuracy on hard ones. This paper introduces QBAT, a query-aware budget autotuner designed to resolve this dilemma. By analyzing query-specific features offline, QBAT dynamically allocates an appropriate budget for each query. We explore two predictive models: a highly accurate gradient-boosted decision tree and a simple, interpretable heuristic formula derived using the AlphaEvolve framework. These models can optimize budget allocation for both system performance or recall consistency priorities. Evaluations on large-scale datasets demonstrate that QBAT reduces total searched budget by up to 68.8% in the consistency mode on ScaNN, the state-of-the-art clustering-based ANNS method, while simultaneously enforcing a strict per-query recall target, a scenario where static budgets are notoriously inefficient and wasteful.
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