Research
Research on systems that verify and improve their own work—from AI applications to data platforms and cloud infrastructure.
Current Research
Self-Verifying AI Systems
How can AI systems verify and improve their own work?
When specifications are incomplete, AI systems may make incorrect assumptions and leave users to discover the failures after the fact.
Explore this research area →Related projects
Selected publications
Self-Improving Data Systems
How can data systems use AI, database learning, and workload feedback to improve queries, data pipelines, and system behavior?
Methods for improving queries, data pipelines, and system behavior using AI, database learning, search, and workload feedback—while preserving semantics and operational constraints.
Explore this research area →Related projects
Selected publications
GenRewrite: Query Rewriting via Large Language Models
SIGMOD 2026 · ACM SIGMOD International Conference on Management of Data
Paper ↗Technical Report ↗Project ↗
Cite
@article{a4a53ccf-7851-464a-8346-dc6c0f5823dc,
title = {GenRewrite: Query Rewriting via Large Language Models},
author = {Jie Liu and Barzan Mozafari},
journal = {ACM SIGMOD International Conference on Management of Data},
year = {2026},
doi = {10.1145/3786684}
}SlabCity: Whole-Query Optimization using Program Synthesis
PVLDB 2023 · Proceedings of the VLDB Endowment
Paper ↗Project ↗
Cite
@article{def837ec-1591-44a4-a36d-929442c789f3,
title = {SlabCity: Whole-Query Optimization using Program Synthesis},
author = {Rui Dong and Jie Liu and Yuxuan Zhu and Cong Yan and Barzan Mozafari and Xinyu Wang},
journal = {Proceedings of the VLDB Endowment},
year = {2023},
doi = {10.14778/3611479.3611515}
}QuickSel: Quick Selectivity Learning with Mixture Models
SIGMOD 2020 · ACM SIGMOD International Conference on Management of Data
Paper ↗Technical Report ↗Project ↗
Cite
@inproceedings{ea3d8506-1f2f-40ea-8bcb-6a74f72e12f0,
title = {QuickSel: Quick Selectivity Learning with Mixture Models},
author = {Yongjoo Park and Shucheng Zhong and Barzan Mozafari},
booktitle = {ACM SIGMOD International Conference on Management of Data},
year = {2020},
doi = {10.1145/3318464.3389727}
}Cloud Optimization & FinOps
How can cloud data warehouses and infrastructure deliver better performance at lower cost under changing workloads?
Workload-aware methods for resource sizing, performance optimization, cost control, and operational safeguards across cloud data warehouses and infrastructure.
Explore this research area →Selected publications
GenRewrite: Query Rewriting via Large Language Models
SIGMOD 2026 · ACM SIGMOD International Conference on Management of Data
Paper ↗Technical Report ↗Project ↗
Cite
@article{a4a53ccf-7851-464a-8346-dc6c0f5823dc,
title = {GenRewrite: Query Rewriting via Large Language Models},
author = {Jie Liu and Barzan Mozafari},
journal = {ACM SIGMOD International Conference on Management of Data},
year = {2026},
doi = {10.1145/3786684}
}Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning
SIGMOD Demo 2023 · ACM SIGMOD International Conference on Management of Data — Demonstration
Paper ↗Project ↗
Cite
@inproceedings{8ede012b-09b5-4196-bc20-95033ee64807,
title = {Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning},
author = {Barzan Mozafari and Radu Alexandru Burcuta and Alan Cabrera and Andrei Constantin and Derek Francis and David Grömling and Alekh Jindal and Maciej Konkolowicz and Valentin Marian Spac and Yongjoo Park and Russell Razo Carranzo and Nicholas Richardson and Abhishek Roy and Aayushi Srivastava and Isha Tarte and Brian Westphal and Chi Zhang},
booktitle = {ACM SIGMOD International Conference on Management of Data — Demonstration},
year = {2023},
doi = {10.1145/3555041.3589681}
}SlabCity: Whole-Query Optimization using Program Synthesis
PVLDB 2023 · Proceedings of the VLDB Endowment
Paper ↗Project ↗
Cite
@article{def837ec-1591-44a4-a36d-929442c789f3,
title = {SlabCity: Whole-Query Optimization using Program Synthesis},
author = {Rui Dong and Jie Liu and Yuxuan Zhu and Cong Yan and Barzan Mozafari and Xinyu Wang},
journal = {Proceedings of the VLDB Endowment},
year = {2023},
doi = {10.14778/3611479.3611515}
}Selected Earlier Work
Predictable Database Systems
The foundational research lineage spanning performance diagnosis, workload modeling, robust design, approximate analytics, and predictable database behavior.
Efficient and Scalable Machine Learning
Machine learning systems for efficient and scalable learning, including communication-efficient learning, distributed optimization, reinforcement learning, bandit models, and transformer memory.
Database Learning
Database-learning methods that improve estimation and reduce computation using workload history.
Interactive Analytics at Scale
Systems for interactive analytics over massive data with explicit guarantees on accuracy and response time.