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Defining the technology of today and tomorrow.

Philosophy

We strive to create an environment conducive to many different types of research across many different time scales and levels of risk.
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Our researchers drive advancements in computer science through both fundamental and applied research.
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Foundational ML & Algorithms

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Algorithms & Theory

Data Management

Data Mining & Modeling

Information Retrieval & the Web

Machine Intelligence

Machine Perception

Machine Translation

Natural Language Processing

Speech Processing

Computing Systems & Quantum AI

Distributed Systems & Parallel Computing

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Software Engineering

Software Systems

Distributed Systems & Parallel Computing

Hardware & Architecture

Mobile Systems

Networking

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Robotics

Security, Privacy, & Abuse Prevention

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Climate & Sustainability

Economics & Electronic Commerce

Education Innovation

General Science

Health & Bioscience

Human-Computer Interaction and Visualization
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We regularly open-source projects with the broader research community and apply our developments to Google products.
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Publishing our work allows us to share ideas and work collaboratively to advance the field of computer science.
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Resources

We make products, tools, and datasets available to everyone with the goal of building a more collaborative ecosystem.
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Student programs

Supporting the next generation of researchers through a wide range of programming.
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Faculty programs

Participating in the academic research community through meaningful engagement with university faculty.
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Faculty programs

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Connecting with the broader research community through events is essential for creating progress in every aspect of our work.
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Blog

Brent Bryan

Brent Bryan

Research Areas

Machine Intelligence

Authored Publications

Google

Sleeping Experts and Bandits with Stochastic Action Availability and Adversarial Rewards

Varun Kanade

H. Brendan McMahan

Brent Bryan

Proceedings of the 12th International Conference on Artificial Intelligence and Statistic (AISTATS) (2009)

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Actively Learning Level-Sets of Composite Functions

Brent Bryan

Jeff Schneider

ICML 2008: International Conference on Machine Learning

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Efficiently Computing Minimax Expected-Size Confidence Regions

Brent Bryan

H. Brendan McMahan

Chad M. Schafer

Jeff Schneider

Proc. 24th ICML, ACM, Corvalis (2007), pp. 97-104

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