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Rajat Monga

Co-author of the TensorFlow white paper and the TensorFlow system paper from Google Brain

Rajat Monga is a co-author of the TensorFlow white paper, which lists its authors at Google Research and describes the interface and implementation built at Google and their open-source release under the Apache 2.0 license. Monga is also a co-author of the 2016 paper TensorFlow: A system for large-scale machine learning, whose authors are listed at Google Brain and which describes how TensorFlow builds on experience with Google's first-generation system, DistBelief.12

At a glance

Current role
None confirmed in the sources
Work listed
1
Sources cited
5
Profile reviewed

Research

Rajat Monga is a co-author, with Jeffrey Dean, Greg Corrado and colleagues, of "Large Scale Distributed Deep Networks" (NIPS 2012), which presents DistBelief, a software framework for training deep networks with billions of parameters on computing clusters. The later TensorFlow system paper describes TensorFlow as building on experience with DistBelief, Google's first-generation system.32

With Jianmin Chen, Xinghao Pan, Samy Bengio and Rafal Jozefowicz, Monga co-authored "Revisiting Distributed Synchronous SGD" (2016), which revisits synchronous optimization for distributed training of deep learning models.4

Open-source work

Monga is a co-author of the TensorFlow white paper, which describes TensorFlow's interface and implementation and its open-source release under the Apache 2.0 license, and of the 2016 paper "TensorFlow: A system for large-scale machine learning". Monga also co-authored the 2019 paper on TensorFlow Eager, a multi-stage, Python-embedded domain-specific language that extends TensorFlow for interactive research and production.125

Work and contributions

  • TensorFlow Open-source code

    Co-author of the TensorFlow white paper and of the 2016 paper TensorFlow: A system for large-scale machine learning; the white paper describes TensorFlow's release as open source under the Apache 2.0 license.12

Timeline

  1. Co-authored "Large Scale Distributed Deep Networks", presented at NIPS 2012.3
  2. Co-authored the TensorFlow white paper on large-scale machine learning on heterogeneous systems.1
  3. Co-authored "Revisiting Distributed Synchronous SGD".4
  4. Co-authored "TensorFlow: A system for large-scale machine learning".2
  5. Co-authored the TensorFlow Eager paper on a Python-embedded DSL for machine learning.5

Documented roles

The sources do not confirm a current role; these are the roles they document.

Companies in their history

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References

5 sources · profile reviewed Oct 21, 2026 · data updated Oct 21, 2026

  1. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems (arXiv 1603.04467)arXiv · Paper · published Mar 14, 2016 · accessed Oct 21, 2026
  2. TensorFlow: A system for large-scale machine learning (arXiv 1605.08695)arXiv · Paper · published May 27, 2016 · accessed Oct 21, 2026
  3. Large Scale Distributed Deep Networks (NIPS 2012 proceedings)NeurIPS Proceedings · Paper · published 2012 · accessed Oct 21, 2026
  4. Revisiting Distributed Synchronous SGD (arXiv 1604.00981)arXiv · Paper · published Apr 4, 2016 · accessed Oct 21, 2026
  5. TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning (arXiv 1903.01855)arXiv · Paper · published Feb 27, 2019 · accessed Oct 21, 2026