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Research

Building systems that can verify, optimize, and improve their own work.

Emerging direction

Self-Verifying AI Systems

How does an AI system know when it is actually done?

An emerging research direction on systems that turn requirements into checks, test their own work, expose uncertainty, and recognize when human review is still required.

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Related projects

Self-Verifying Agentic Systems

Research area

AI + Query Intelligence

Can a data system discover better ways to express and execute a query?

AI, large language models, program synthesis, and systems techniques for query rewriting, equivalence, optimization, and learned data-system behavior.

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Related projects

AI for Query and Infrastructure Optimization

Database Learning: QuickSel and BlinkML

Selected publications

Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning

Barzan Mozafari, Radu Alexandru Burcuta, Alan Cabrera, Andrei Constantin, Derek Francis, David Grömling, Alekh Jindal, Maciej Konkolowicz, Valentin Marian Spac, Yongjoo Park, Russell Razo Carranzo, Nicholas Richardson, Abhishek Roy, Aayushi Srivastava, Isha Tarte, Brian Westphal, Chi Zhang

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

Rui Dong, Jie Liu, Yuxuan Zhu, Cong Yan, Barzan Mozafari, Xinyu Wang

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

Yongjoo Park, Shucheng Zhong, Barzan Mozafari

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}
}

Research area

Autonomous Cloud & FinOps

Can cloud data infrastructure continuously balance performance and cost?

Workload-aware optimization, learned control, warehouse sizing, and operational safeguards for cloud data warehouses and data infrastructure.

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Related projects

AI for Query and Infrastructure Optimization

Selected publications

Making Data Clouds Smarter at Keebo: Automated Warehouse Optimization using Data Learning

Barzan Mozafari, Radu Alexandru Burcuta, Alan Cabrera, Andrei Constantin, Derek Francis, David Grömling, Alekh Jindal, Maciej Konkolowicz, Valentin Marian Spac, Yongjoo Park, Russell Razo Carranzo, Nicholas Richardson, Abhishek Roy, Aayushi Srivastava, Isha Tarte, Brian Westphal, Chi Zhang

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}
}

Research area

Predictable & Intelligent Systems

How can a data system make uncertainty, tradeoffs, and future behavior visible?

The foundational research lineage spanning performance diagnosis, workload modeling, robust design, approximate analytics, and predictable database behavior.

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Related projects

Approximate Query Processing and Interactive Analytics: BlinkDB, VerdictDB, and SnappyData

Database Learning: QuickSel and BlinkML

Database Performance Intelligence: DBSeer and DBSherlock

Predictable Database Systems

Robust Database Design: CliffGuard

Transaction Scheduling: VATS and CATS

Selected publications

DMon: Efficient Detection and Correction of Data Locality Problems Using Selective Profiling

Tanvir Ahmed Khan, Ian Neal, Gilles Pokam, Barzan Mozafari, Baris Kasikci

OSDI 2021 · USENIX Symposium on Operating Systems Design and Implementation

Paper ↗Project ↗
Cite
@inproceedings{bd35a4d0-2624-465c-aabb-0cf0d78b3147,
  title = {DMon: Efficient Detection and Correction of Data Locality Problems Using Selective Profiling},
  author = {Tanvir Ahmed Khan and Ian Neal and Gilles Pokam and Barzan Mozafari and Baris Kasikci},
  booktitle = {USENIX Symposium on Operating Systems Design and Implementation},
  year = {2021}
}

QuickSel: Quick Selectivity Learning with Mixture Models

Yongjoo Park, Shucheng Zhong, Barzan Mozafari

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}
}

BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

Yongjoo Park, Jingyi Qing, Xiaoyang Shen, Barzan Mozafari

SIGMOD 2019 · ACM SIGMOD International Conference on Management of Data

Paper ↗Technical Report ↗Project ↗
Cite
@inproceedings{50db23fc-a5a3-4d7f-ac07-2a696129c92f,
  title = {BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees},
  author = {Yongjoo Park and Jingyi Qing and Xiaoyang Shen and Barzan Mozafari},
  booktitle = {ACM SIGMOD International Conference on Management of Data},
  year = {2019},
  doi = {10.1145/3299869.3300077}
}

Join on Samples: A Theoretical Guide for Practitioners

Dawei Huang, Dong Young Yoon, Seth Pettie, Barzan Mozafari

PVLDB 2019 · Proceedings of the VLDB Endowment

Paper ↗Technical Report ↗Project ↗
Cite
@article{6d32861d-ba95-4e8b-91c3-bc689ec60e12,
  title = {Join on Samples: A Theoretical Guide for Practitioners},
  author = {Dawei Huang and Dong Young Yoon and Seth Pettie and Barzan Mozafari},
  journal = {Proceedings of the VLDB Endowment},
  year = {2019},
  doi = {10.14778/3372716.3372726}
}