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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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Philosophy

People

Our researchers drive advancements in computer science through both fundamental and applied research.
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People
Research areas

Explore all research areas

Explore all research areas

Foundational ML & Algorithms

Algorithms & Theory

Data Management

Data Mining & Modeling

Information Retrieval & the Web

Machine Intelligence

Machine Perception

Machine Translation

Natural Language Processing

Speech Processing

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

Hardware & Architecture

Mobile Systems

Networking

Quantum Computing

Robotics

Security, Privacy, & Abuse Prevention

Software Engineering

Software Systems

Distributed Systems & Parallel Computing

Hardware & Architecture

Mobile Systems

Networking

Quantum Computing

Robotics

Security, Privacy, & Abuse Prevention

Software Engineering

Software Systems

Science, AI & Society

Climate & Sustainability

Economics & Electronic Commerce

Education Innovation

General Science

Health & Bioscience

Human-Computer Interaction and Visualization

Climate & Sustainability

Economics & Electronic Commerce

Education Innovation

General Science

Health & Bioscience

Human-Computer Interaction and Visualization
Projects

We regularly open-source projects with the broader research community and apply our developments to Google products.
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Projects

Publications

Publishing our work allows us to share ideas and work collaboratively to advance the field of computer science.
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Publications

Resources

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

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

Faculty programs

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

Conferences & events

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

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Careers
Blog

Lin Ning

Lin Ning

Research Areas

Authored Publications

Google

USER-LLM: Efficient LLM Contextualization with User Embedding

Lin Ning

Luyang Liu

Jiaxing Wu

Neo Wu

Devora Berlowitz

Sushant Prakash

Bradley Green

Shawn O'Banion

Jun Xie

ArXiv (2024) (to appear)

Mixed Federated Learning: Joint Decentralized and Centralized Learning

Sean Augenstein

Andrew Hard

Lin Ning

Karan Singhal

Satyen Kale

Kurt Partridge

Rajiv Mathews

Arxiv (2022) (to appear)

What Do We Mean by Generalization in Federated Learning?

Honglin Yuan

Warren Morningstar

Lin Ning

Karan Singhal

International Conference on Learning Representations (ICLR) (2022)

EANA: Reducing Privacy Risk on Large-scale Recommendation Models

Devora Berlowitz

Lin Ning

Mei Chen

QiQi Xue

Shuang Song

Steve Chien

16th ACM Conference on Recommender Systems (2022)

Learning Federated Representations and Recommendations with Limited Negatives

Lin Ning

Karan Singhal

Ellie Zhou

Sushant Prakash

(2021)

Learning Federated Representations and Recommendations with Limited Negatives

Lin Ning

Karan Singhal

Ellie Zhou

Sushant Prakash

1st NeurIPS Workshop on New Frontiers in Federated Learning (NFFL 2021) (2021)

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