Photo credit: Hector Garcia-Molina
I study data and data-intensive systems as a member of the Stanford Future Data Systems group.
Bio: Peter Bailis is an assistant professor of Computer Science at Stanford University. Peter's research in the Future Data Systems group focuses on the design and implementation of next-generation, post-database data-intensive systems. His work spans large-scale data management, distributed protocol design, and architectures for high-volume complex decision support. He is the recipient of an NSF Graduate Research Fellowship, a Berkeley Fellowship for Graduate Study, best-of-conference citations for research appearing in both SIGMOD and VLDB, and the CRA Outstanding Undergraduate Researcher Award. He received a Ph.D. from UC Berkeley in 2015 and an A.B. from Harvard College in 2011, both in Computer Science.
Teaching: CS245 (Winter 2017) | CS345 (Fall 2016)
Big bet: MacroBase represents the future of high-volume online analytics.
410 Gates Hall
Stanford University
Stanford, CA 94305
Office Hours (Winter 2017):
Wed 3-4PM, Gates 410

Our group's research is generously supported in part by Toyota, Intel, RWE AG, Visa, Keysight Technologies, Facebook, and VMWare.


In addition to my core affiliation in the Future Data Systems group, I am a member of the Stanford InfoLab, the Stanford Data Science Initiative, the Stanford SystemX Alliance, and the Secure Internet of Things project.

Thanks to Keysight Technologies for supporting our research through the Stanford SystemX Alliance!
Continued progress on an absolute ton of research in the pipeline. Many submissions in the works; please feel free to ping.
Our paper on challenges and opportunities in prioritizing attention in fast data streams will appear at CIDR 2017 in January. See you in Santa Cruz!
Thanks to Facebook for beginning to support our research in the Future Data Systems group!
The third installment of Research for Practice is now online at ACM Queue!
Our paper on MacroBase's core architecture will appear at SIGMOD 2017! Congratulations to the students on a first-round acceptance without revisions.
Welcome to our fall quarter rotators! Exciting new projects on speeding up video classification and unsupervised anomaly detection in fast data streams.
New post on MacroBase on the MIT ISTC Big Data blog.
Honored to receive an inaugural Faculty Research Award from VISA! Thank you for supporting our research on MacroBase.
Tentative schedule for CS345S: Data-Intensive Systems for the Next 1000x ( (August 2016) posted!
Professor Matei Zaharia and co-advisee Firas Abuzaid join Stanford CS and the Infolab. Welcome to campus, and here's to many more years of great research collaboration!
New MacroBase results on the arXiv, including new results from production, video monitoring, time-series analysis.
I talked about the Infolab and Future Data Systems on the Software Engineering Daily podcast.
New video interview with Data By The Bay.
Newest issue of Research for Practice is up on ACM Queue.
What's MacroBase about? We've posted a one-page overview.
Many thanks to VMWare for supporting Future Data Systems via an Early Career Faculty Award!
Excited to unveil Research for Practice: expert-curated guides to the best of CS research in ACM Queue/CACM! Keep on the lookout for more issues.
Growing excitement around MacroBase: UI upgrades, new time-series feature transforms, many talks and industrial use cases. Stay tuned for updates on the arXiv.
More news

Selected Publications Full List · Google Scholar

MacroBase: Prioritizing Attention in Fast Data
Prioritizing Attention in Fast Data: Principles and Promise
Scalable Atomic Visibility with RAMP Transactions
Coordination Avoidance in Database Systems
Feral Concurrency Control: An Empirical Investigation of Modern Application Integrity
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox
Readings in Database Systems, 5th Edition
Coordination Avoidance in Distributed Databases
Highly Available Transactions: Virtues and Limitations
Quantifying Eventual Consistency with PBS
Scalable Atomic Visibility with RAMP Transactions
The Network is Reliable: An Informal Survey of Real-World Communications Failures
Quantifying Eventual Consistency with PBS
Consistency without Borders
PBS at Work: Advancing Data Management with Consistency Metrics
Bolt-on Causal Consistency
HAT, not CAP: Towards Highly Available Transactions
Eventual Consistency Today: Limitations, Extensions, and Beyond
Probabilistically Bounded Staleness for Practical Partial Quorums
The Potential Dangers of Causal Consistency and an Explicit Solution
Programming Micro-aerial Vehicle Swarms with Karma
Dimetrodon: Processor-level Preventive Thermal Management via Idle Cycle Injection
Positional Communication and Private Information in Honeybee Foraging Models

You can follow me on Twitter at @pbailis.