PeopleofSuperintelligence

Researcher

Xiaoqiang Zheng

Xiaoqiang Zheng is a machine-learning and visualization researcher who coauthored the TensorFlow system at Google Brain. Working with Alex Pang from 2002 to 2005, he published IEEE Visualization papers on tensor deformation, HyperLIC and 3D tensor topology, and earned a PhD in the VIS Group in 2005. After the TensorFlow API and reference implementation became open source in November 2015, he coauthored the 2016 whitepaper and OSDI system paper on dataflow graphs across CPUs, GPUs and TPUs. In 2018, he coauthored a TensorFlow programming model for dynamic control flow and helped with MnasNet's mobile model search, before contributing discussions to the GPipe pipeline-parallel training library in March 2019.

Works
26
Badges

Publication numbers from OpenAlex, as of 22 October 2026. Citations and h-index are hidden because this OpenAlex record may be incomplete or mix in other authors.

Timeline

  1. 2002–2003

    Volume deformation and HyperLIC

    In 2002, he and Alex Pang publish "Volume Deformation For Tensor Visualization" at IEEE Visualization 2002. With Pang, he publishes "HyperLIC" at the 14th IEEE Visualization Conference (VIS 2003) in 2003.

  2. 2004

    Topological lines in tensor fields

    In October 2004, he and Alex Pang present "Topological Lines in 3D Tensor Fields" at the 15th IEEE Visualization Conference (VIS 2004) in Austin.

  3. 2005

    VIS Group PhD and tensor topology

    He earns a PhD in the VIS Group at UC Santa Cruz in 2005. At the 16th IEEE Visualization Conference (VIS 2005) in Minneapolis in October 2005, he presents "Topological Structures of 3D Tensor Fields" with Beresford N. Parlett and Alex Pang, and "2D Asymmetric Tensor Analysis" with Pang.

  4. 2015–2016

    TensorFlow goes open source

    The TensorFlow API and reference implementation become open source under the Apache 2.0 license in November 2015, and Zheng coauthors the paper that describes the system in 2016.

  5. 2016

    TensorFlow at OSDI

    At OSDI in 2016, he coauthors the TensorFlow system paper with Google Brain colleagues. The paper describes dataflow graphs for distributed computation across clusters and devices, including multicore CPUs, GPUs and Tensor Processing Units (TPUs).

  6. 2018

    Dynamic control flow in TensorFlow

    In 2018, he coauthors "Dynamic Control Flow in Large-Scale Machine Learning", presenting a programming model for distributed machine learning implemented in TensorFlow.

  7. August 2018

    MnasNet mobile search

    In August 2018, alongside Wen Wang, Yifeng Lu, and the TensorFlow Lite and Google Brain teams, he helps with MnasNet, a reinforcement-learning search for mobile CNN architectures that measures speed on real phones.

  8. March 2019

    Ideas for GPipe

    In March 2019, he contributes discussions and inspirations to GPipe, an open-source library that uses pipeline parallelism to train large-scale neural network models.

  1. The median h-index is 48 at universities and 14 at frontier AI labsIncludes University of California, Santa Cruz. The median h-index is 48 at universities and 14 at frontier AI labs. We compare publishing careers, recent citations, dual appointments and job moves across 2,420 university researchers and 308 frontier-lab researchers.
  2. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes University of California, Santa Cruz. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  3. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University of California, Santa Cruz. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  4. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University of California, Santa Cruz. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.

Sources and credits

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  1. xiaoqiang zheng ixdf.org. Checked 22 October 2026.
  2. VIS Group - People sites.google.com. Checked 22 October 2026.
  3. [1603.04467] TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems arxiv.org. Checked 22 October 2026.
  4. TensorFlow: A system for large-scale machine learning research.google. Checked 22 October 2026.
  5. TensorFlow: A System for Large-Scale Machine Learning | USENIX usenix.org. Checked 22 October 2026.
  6. Xiaoqiang Zheng | USENIX usenix.org. Checked 22 October 2026.
  7. [1805.01772] Dynamic Control Flow in Large-Scale Machine Learning arxiv.org. Checked 22 October 2026.
  8. MnasNet: Towards Automating the Design of Mobile Machine Learning Models research.google. Checked 22 October 2026.
  9. Introducing GPipe, an Open Source Library for Efficiently Training Large-scale Neural Network Models research.google. Checked 22 October 2026.