World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
34
Citations
83799
World Ranking
9104
National Ranking
2537

Overview

Daniel Ramage is affiliated with Google in the United States and focuses primarily on research in computer science with a specialization in artificial intelligence and related subfields. Their body of work spans topics such as privacy-preserving technologies, cryptography, adversarial robustness in machine learning, and blockchain technology applications and security.

The scientist has contributed notably to the field of federated learning and privacy. Their recent publications include:

  • "Advances and Open Problems in Federated Learning," 2020, Foundations and Trends® in Machine Learning
  • "Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning," 2022, 2022 IEEE Symposium on Security and Privacy (SP)
  • "Federated Learning and Privacy," 2021, Queue
  • "Federated learning and privacy," 2022, Communications of the ACM
  • "Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning," 2021, arXiv (Cornell University)

Key topics covered within Daniel Ramage's research include:

  • Privacy-Preserving Technologies in Data
  • Cryptography and Data Security
  • Adversarial Robustness in Machine Learning
  • Blockchain Technology Applications and Security
  • Privacy, Security, and Data Protection
  • Advanced Data Storage Technologies
  • Mobile Crowdsensing and Crowdsourcing

Their frequent co-authors, reflecting ongoing collaborations, are:

  • Peter Kairouz
  • Kallista Bonawitz
  • Hubert Eichner
  • Marco Gruteser
  • Adrià Gascón

Daniel Ramage has appeared in multiple publication venues, with a concentration in the following:

  • arXiv (Cornell University)
  • Foundations and Trends® in Machine Learning
  • 2022 IEEE Symposium on Security and Privacy (SP)
  • Queue
  • Communications of the ACM

Their academic contributions primarily focus on the computer science domain with research output concentrated in artificial intelligence, information systems, computer networks and communications, sociology and political science, and computer science applications, outlining a multidisciplinary approach in their work.

Best Publications

  • Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

    Paul Shannon;Andrew Markiel;Owen Ozier;Nitin S. Baliga

  • Communication-Efficient Learning of Deep Networks from Decentralized Data

    H. Brendan McMahan;Eider Moore;Daniel Ramage;Seth Hampson

  • Advances and Open Problems in Federated Learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Practical Secure Aggregation for Privacy Preserving Machine Learning.

    Kallista A. Bonawitz;Vladimir Ivanov;Ben Kreuter;Antonio Marcedone

  • Towards Federated Learning at Scale: System Design

    Kallista A. Bonawitz;Hubert Eichner;Wolfgang Grieskamp;Dzmitry Huba

  • Federated Optimization: Distributed Machine Learning for On-Device Intelligence

    Jakub Konečný;H. Brendan McMahan;Daniel Ramage;Peter Richtarik

  • Labeled LDA: A supervised topic model for credit attribution in multi-labeled corpora

    Daniel Ramage;David Hall;Ramesh Nallapati;Christopher D. Manning

  • Federated Learning of Deep Networks using Model Averaging

    H. Brendan McMahan;Eider Moore;Daniel Ramage;Blaise Aguera y Arcas

  • Federated Learning for Mobile Keyboard Prediction

    Andrew Hard;Chloé M Kiddon;Daniel Ramage;Francoise Beaufays

  • Advances and open problems in federated learning

    Peter Kairouz;H. Brendan McMahan;Brendan Avent;Aurélien Bellet

  • Characterizing Microblogs with Topic Models

    Daniel Ramage;Susan T. Dumais;Daniel J. Liebling

  • Learning Differentially Private Recurrent Language Models

    H. Brendan McMahan;Daniel Ramage;Kunal Talwar;Li Zhang

  • Federated Optimization: Distributed Optimization Beyond the Datacenter

    Jakub Konečný;H. Brendan McMahan;Daniel Ramage

  • Applied Federated Learning: Improving Google Keyboard Query Suggestions

    Timothy Yang;Galen Andrew;Hubert Eichner;Haicheng Sun

  • Social tag prediction

    Paul Heymann;Daniel Ramage;Hector Garcia-Molina

  • #TwitterSearch: a comparison of microblog search and web search

    Jaime Teevan;Daniel Ramage;Merredith Ringel Morris

  • Clustering the tagged web

    Daniel Ramage;Paul Heymann;Christopher D. Manning;Hector Garcia-Molina

  • Interpretation and trust: designing model-driven visualizations for text analysis

    Jason Chuang;Daniel Ramage;Christopher Manning;Jeffrey Heer

  • Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning

    Unknown

  • Partially labeled topic models for interpretable text mining

    Daniel Ramage;Christopher D. Manning;Susan Dumais

  • Practical Secure Aggregation for Federated Learning on User-Held Data

    Kallista A. Bonawitz;Vladimir Ivanov;Ben Kreuter;Antonio Marcedone

Frequent Co-Authors

H. Brendan McMahan
H. Brendan McMahan Google (United States)
Christopher D. Manning
Christopher D. Manning Stanford University
Li Zhang
Li Zhang Google (United States)
Kunal Talwar
Kunal Talwar Apple (United States)
Dan Jurafsky
Dan Jurafsky Stanford University
Francoise Beaufays
Francoise Beaufays Google (United States)
Susan T. Dumais
Susan T. Dumais Microsoft (United States)
Farinaz Koushanfar
Farinaz Koushanfar University of California, San Diego
Dawn Song
Dawn Song University of California, Berkeley

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