PETER BAILIS
pdb
Photo credit: Hector Garcia-Molina
I study data and data-intensive systems as a member of DAWN and the Stanford Future Data Systems group.
🔥🔥 DAWN is Data Analytics for What's Next 🔥🔥
Bio: Peter Bailis is an assistant professor of Computer Science at Stanford University. Peter's research in the Future Data Systems group and DAWN project focuses on the design and implementation of post-database data-intensive systems. He is the recipient of the ACM SIGMOD Jim Gray Doctoral Dissertation Award, 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: CS145 (Fall 2017) | CS245 (Winter 2017) | CS345 (Fall 2016)
MacroBase is a new analytics engine: read the one-pager.
Contact:
Office:
410 Gates Hall
Stanford University
Stanford, CA 94305
Support

Our group's research is generously supported in part by the affiliate members and other supporters of the Stanford DAWN project—Intel, Microsoft, Teradata, and VMware—as well as the AHPCRC, Toyota, Visa, Keysight Technologies, Hitachi, Northrop Grumman, Facebook, Juniper, and NetApp.

Affiliations

I am a member of the Stanford DAWN project. Day-to-day, I am a professor in the Future Data Systems group and sit in the Stanford InfoLab.

News
Posted NoScope slides from VLDB 2017.
8/30/2017
New blog post on learning sparse models with random projections and compressive sensing.
8/29/2017
New blog post on automatic time series smoothing with ASAP (VLDB 2017).
8/7/2017
Two VLDB 2017 camera-ready papers available: NoScope and ASAP
8/4/2017
NoScope repo updated with tutorial and docs!
8/3/2017
New arXiv preprint on DROP: Dimensionality Reduction OPtimization via PCA and progressive sampling
8/1/2017
MacroBase: Prioritizing Attention in Fast Data received a "Best of SIGMOD 2017" citation! Thanks, Dan and SIGMOD PC!
7/14/2017
NoScope (optimizing CNN-based video analytics via specialization) accepted to VLDB 2017!
7/3/2017
Thanks to NetApp for awarding a NetApp Faculty Fellowship to support our work on prioritizing attention in fast data streams!
6/28/2017
New work with Vatsal Sharan, Kai Sheng Tai, and Greg Valiant on learning sparse models by exploiting sparsity now on arXiv!
6/25/2017
New blog post on accelerating neural network inference over video with NoScope
6/22/2017
ASAP -- automatic smoothing for time-series visualization -- accepted to VLDB 2017. See you in Munich!
6/15/2017
Kexin's Monitorama 2017 talk on time series smoothing is now online!
6/10/2017
New arXiv pre-print on speeding up matrix factorization serving by up to 6x: should you build an index or just call BLAS? SimDex can answer.
6/5/2017
Three PhD students join Future Data Systems! Welcome Cody Coleman, Daniel Kang, and Kai Sheng Tai.
6/3/2017
DAWN project whitepaper on systems for usable ML now available on arXiv.
5/23/2017
Slides and audio for SIGMOD Jim Gray Doctoral Dissertation Award talk posted!
5/18/2017
Slides and video for Edward's SIGMOD 2017 talk on fast Kernel Density Classification now online.
5/17/2017
Thanks to the ACM for awarding an honorable mention for the ACM Doctoral Dissertation Award! This is a real honor.
5/17/2017
Slides for Todd's ACIDRain talk at SIGMOD 2017 posted!
5/16/2017
More news

Selected Publications · Google Scholar

Preprint
DROP: Dimensionality Reduction Optimization for Time Series
There and Back Again: A General Approach to Learning Sparse Models
SimDex: Exploiting Model Similarity in Exact Matrix Factorization Recommendations
Infrastructure for Usable Machine Learning: The Stanford DAWN Project
2017
NoScope: Optimizing Neural Network Queries over Video at Scale
ASAP: Prioritizing Attention via Time Series Smoothing
MacroBase: Prioritizing Attention in Fast Data
Prioritizing Attention in Fast Data: Principles and Promise
2016
Scalable Atomic Visibility with RAMP Transactions
2015
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
2014
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
2013
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
2012
Probabilistically Bounded Staleness for Practical Partial Quorums
The Potential Dangers of Causal Consistency and an Explicit Solution
2011
Programming Micro-aerial Vehicle Swarms with Karma
Dimetrodon: Processor-level Preventive Thermal Management via Idle Cycle Injection
2010
Positional Communication and Private Information in Honeybee Foraging Models

You can follow me on Twitter at @pbailis.