Tarique Siddiqui
Principal Researcher
About
I am a researcher in the Data Systems group at Microsoft Research, Redmond.
My research interest lies in improving the performance of database systems with focus on:
- Query Optimization. Check out my recent work in this area:
- Database Tuning. Recent work includes:
- Scalable index tuning using ML techniques: Filtering and costing configurations, Efficiently searching configurations
- Workload compression for index tuning
- Workload forecasting
My research has been awarded with the best paper at ACM SIGMOD, Research Highlight, as well as featured in CACM. Prior to joining MSR, I completed my Ph.D. from the University of Illinois at Urbana, Champaign (UIUC), advised by Aditya Parameswaran.
Selected Professional Activities:
- Local Organizing Chair, ACM SIGMOD 2023
- VLDB Demo Award Committee 2021
- Panelist VLDB 2021
- PC/Editorial Board:
- SIGMOD: 2026, 2025, 2024, 2023, 2021
- VLDB: 2026, 2025, 2024, 2023, 2022, 2021 (demo)
- VLDB Journal: 2025-2027
- ICDE (2023, 2022); SIGKDD (2023, 2022); SIGIR (2023, 2022)
Recent news:
- 11/2024: QURE, an AI-assisted and formally verified UDF to SQL translation technique at SIGMOD 2025.
- 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.