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Sanjay Ghemawat

Sanjay Ghemawat has 2 papers in our library, in 2016; often with Andy Davis, Jeff Dean, Martin Abadi.

2 papers in our library · 2016

OpenAlex · ORCID · h-index 26 · 63 works · 60,530 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 7,522 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #109

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢GoogleMountain View · United States2000–2025 · 17 paperscurrent🎓Massachusetts Institute of TechnologyCambridge · United States1991–2003 · 11 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksHardware and Architecture · 2 worksInformation Systems · 2 worksComputer Networks and Communications · 1 workComputer Vision and Pattern Recognition · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Machine learning · 2 works

 Works by year

2010s

20162 papers

 Papers

The most cited first (OpenAlex and our library).

  1. TensorFlow: A system for large-scale machine learning (2016 · 12,711 citations)
    The paper presents TensorFlow, Google Brain's successor to its earlier DistBelief system. It addresses a gap among existing tools. Single-machine frameworks such as Caffe, Theano and Torch are flexible but do not scale across clusters. Parameter-server systems scale, but they hard-code how shared model state is…
  2. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (2016 · 9,512 citations)
    Google's first distributed deep-learning system, DistBelief, was powerful but rigid. Separate tools for large-scale training and small-scale deployment also caused maintenance problems and leaky abstractions. TensorFlow, the second-generation system, addresses this with one programming model that runs on phones…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. TensorFlow: A system for large-scale machine learningarXiv:1605.08695Martin Abadi, Paul Barham, Jianmin Chen, et al.2016 · 12,711 citations
  2. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed SystemsarXiv:1603.04467Martín Abadi, Ashish Agarwal, Paul Barham, et al.2016 · 9,512 citations
  3. Pathways: Asynchronous Distributed Dataflow for ML ↗arXiv (Cornell University) · no summary yetPaul Barham et al.2022 · 85 citations

 Co-authors here

10 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 7,522 times (of the 149,439 papers whose bibliographies we read); 1,272 citing papers with a page here →.

Updated

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