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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

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Machine Translation

Natural Language Processing

Speech Processing

Computing Systems & Quantum AI

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Security, Privacy, & Abuse Prevention

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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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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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Blog

Scott B. Huffman

Scott B. Huffman

Research Areas

Information Retrieval and the Web

Authored Publications

Google

Good Abandonment in Mobile and PC Internet Search

Jane Li

Scott Huffman

Akihito Tokuda

32nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM (Association for Computing Machinery), 2 Penn Plaza, Suite 701, New York 10121-0701 (2009), pp. 43-50

How evaluator domain expertise affects search result relevance

Kenneth A. Kinney

Scott B. Huffman

Juting Zhai

Conference on Information and Knowledge Management (2008), pp. 591-598

Preview

Multiple-Signal Duplicate Detection for Search Evaluation

Scott Huffman

April Lehman

Alexei Stolboushkin

Howard Wong-Toi

Fan Yang

Hein Roehrig

Proceedings of 30th Annual International ACM SIGIR Conference, ACM (2007), pp. 223-230

Preview

How well does result relevance predict session satisfaction?

Scott B. Huffman

Michael Hochster

Proceedings of the 30th annual international ACM SIGIR, ACM, Amsterdam (2007), pp. 567-574

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