Tarique Siddiqui

About

I am a researcher in the Data Systems group (previously DMX) at Microsoft Research, Redmond with interests in database systems and analytics.

My current focus is on query optimization and database tuning, spanning sub-areas such as:

• query optimization and execution of imperative code in database systems.
• workload management and characterization techniques, e.g., workload compression, workload forecasting, and workload simplification/reduction.
• ML for scalable index tuning and cost modeling.

Prior to joining MSR, I completed my Ph.D. from the University of Illinois at Urbana, Champaign (UIUC), advised by Aditya Parameswaran.

Recent news:

• 11/2023: Zippy, a cache-efficient top-k aggregation technique at VLDB 2024.
• 11/2023: WRed, a workload reduction technique (complementing workload compression) for scalable index tuning at SIGMOD 2024.
• 11/2023: SIBYL, a new workload forecasting technique at SIGMOD 2024.
• 07/2022: CACM Research Highlight article on “Expressive and Scalable Visual Querying“.
• 04/2022: DISTILL, a data-driven filtering and costing approach for scalable index tuning at VLDB, 2022.
• 03/2022: ISUM, an efficient workload compression technique at SIGMOD, 2022.
• 03/2022: Budget-aware Index Tuning with Reinforcement Learning at SIGMOD, 2022 (led by Wentao Wu).
• 07/2021: COMPARE, an efficient in-database technique for accelerating groupwise comparison at VLDB, 2021.