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Defining the technology of today and tomorrow.

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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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Natural Language Processing

Speech Processing

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Distributed Systems & Parallel Computing

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Climate & Sustainability

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Education Innovation

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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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We make products, tools, and datasets available to everyone with the goal of building a more collaborative ecosystem.
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Shaping the future, together.
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Supporting the next generation of researchers through a wide range of programming.
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Participating in the academic research community through meaningful engagement with university faculty.
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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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Careers
Blog

Greg Kochanski

MIT Physics 1982 S.B., 1987 Ph.D. AT&T Bell Laboratories & Lucent Technologies Bell Laboratories, 1987-2002. University of Oxford Faculty of Linguistics 2003-2011.

Research Areas

Authored Publications

Google

Gradientless Descent: High-Dimensional Zeroth-Order Optimization

Chansoo Lee

Daniel Golovin

Greg Kochanski

John Elliot Karro

Richard Song

Richard Zhang

ICLR 2020 (2020)

Bayesian Optimization for a Better Dessert

Benjamin Solnik

Daniel Golovin

Greg Kochanski

John Elliot Karro

Subhodeep Moitra

D. Sculley

Proceedings of the 2017 NIPS Workshop on Bayesian Optimization, December 9, 2017, Long Beach, USA (to appear)

Black Box Optimization via a Bayesian-Optimized Genetic Algorithm

Daniel Golovin

Greg Kochanski

John Elliot Karro

Advances in Neural Information Processing Systems 30 (NIPS 2017) (to appear)

Google Vizier: A Service for Black-Box Optimization

Daniel Golovin

Benjamin Solnik

Subhodeep Moitra

Greg Kochanski

John Elliot Karro

D. Sculley

ACM (2017), pp. 1487-1495

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