I build data-intensive systems that are more scalable, more robust, and more predictable. I draw from advanced statistical models to deliver practical database solutions to real-world problems. In particular, I adapt concepts and tools from applied statistics, optimization theory, and machine learning.

Barzan Mozafari is an Assistant Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, where he leads a research group designing the next generation of scalable databases using advanced statistical models. Prior to that, he was a Postdoctoral Associate at MIT. He earned his Ph.D. in Computer Science from UCLA in 2011. His research career has led to many successful open-source projects, including CliffGuard (the first robust framework for database tuning), DBSeer (the first automated database diagnosis tool), and BlinkDB (the first massively parallel approximate query engine). He has won the National Science Foundation CAREER award, as well as several best paper awards in ACM SIGMOD and EuroSys. He is also a co-founder of DBSeer and a strategic advisor to SnappyData, a company that commercializes the ideas introduced by BlinkDB.

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exciting news
SnappyData v0.1 open sourced and available for Download! SnappyData is the commercialization of our approximate query processing techniques, providing an integrated framework for interactive analytics, transactions and stream processing. Check out our release!
$3.46M to combine supercomputer simulations with big data: NSF and University of Michigan have jointly sponsored our ConFlux project: a massively parallel system for solving open problems in computational physics using large-scale machine learning!
CAREER Award: Designing a Predictable Database - An Overlooked Virtue Database research has mostly focused on improving the raw performance of database systems, while neglecting the predictability of their performance. NSF has funded us to rethink this traditional architecture, and build a new class of databases that guarantee predictability.
Vehicle-aware data management for autonomous cars Testing autonomous vehicles is an extremely involved task, as an enormous amount of data is constantly collected and processed by thousands of sensors. NSF has funded us to design a smart black-box for cars that can use effective data collection strategies for maximizing the likelihood of finding various types of faults!
Big Data Summer Institute in Bio-statistics Funded by National Institutes of Health, we have launched our Big Data Summer Institute, where you can learn about Big Data, statistics and bio-informatics while getting paid! All details and how to apply, can be found here (do NOT email me about this)!
SIGMOD's Best Demo Award: Our ABS system won this year's best demo award at SIGMOD! We also had a number of papers on Approximate Query Processing at this year's SIGMOD (2014).
Openings in My Research Group: I am looking for strong PhD students and postdocs! See the details here.
Best Paper Award: Our BlinkDB paper won EuroSys 2013's Best Paper Award!
Best Paper Award: Our paper won SIGMOD 2012's Best Paper Award!
selected projects
DBSeer
Workload intelligence and automatic performance diagnosis for multi-tenant databases and database-as-a-service.
 
BlinkDB
Delivering sub-second latency when querying terabytes and petabytes of data.
 
Crowd-sourcing Big Data
Making crowd-sourcing scale up to tens of millions of tasks.
 
K*SQL and XSeq
High-performance complex event processing on complex data types such as RNA sequences, JSON files, and software traces.

Assistant Professor
Computer Science and Engineering
University of Michigan (Ann Arbor)
Database Group

Computer Science and Engineering
4769 Beyster Building
2260 Hayward St.
University of Michigan
Ann Arbor, MI 48109-2121

Phone: (734) 763-3669
Email: mozafari@umich.edu

Office hours: by appointment only

Curriculum vitae · all publications
Archived faculty app: research, teaching

Talks & Travel
Jun 25SIGMOD
Nov 21Google Kirkland
Nov 22University of Washington
May 30Teradata (San Diego)
May 31UCLA
June 23-26SIGMOD
Jan 4-7CIDR

Acknowledgements

Our research is made possible through the generosity of the University of Michigan.

* This website's template is borrowed from Michael Bernstein and Jeffrey Heer.