PeopleofSuperintelligence

Lab leader

Guodong Zhang

Also known as 张国栋

Guodong Zhang is a machine learning researcher and xAI co-founder whose career spans neural network optimization and AI product leadership. He coauthored research on Bayesian neural networks, weight decay, critical batch sizes and deep networks without shortcuts. In 2023, he joined xAI's founding team after work as a DeepMind research scientist and a PhD at the University of Toronto. In March 2026, he left xAI after leading Grok Code and Grok Imagine. As of October 2026, he is a machine learning researcher focused on training, tuning and aligning large language models.

Timeline

  1. 2018

    Noisy natural-gradient training

    He coauthors Noisy Natural Gradient as Variational Inference in 2018, using adaptive weight noise with natural-gradient methods to train Bayesian neural networks.

  2. 2018

    Weight-decay mechanisms

    In a 2018 paper, he and his collaborators identify three mechanisms by which weight decay regularizes neural networks, with effects that depend on the optimization algorithm and architecture.

  3. 2019

    Critical batch sizes

    His 2019 NeurIPS paper with seven coauthors studies how acceleration and preconditioning affect critical batch sizes through experiments and a noisy quadratic model.

  4. 2022

    Deep networks without shortcuts

    In 2022, he and his coauthors develop a tailored activation transformation for deep networks without shortcut connections or normalization layers.

  5. 2023

    xAI's founding team

    In 2023, he joins xAI's founding team with prior work as a DeepMind research scientist and a PhD from the University of Toronto.

  6. March 2026

    Grok Code and Imagine

    In March 2026, he leaves xAI after leading Grok Code and Grok Imagine.

  7. As of October 2026

    Large language model research

    As of October 2026, he is a machine learning and AI researcher focused on training, tuning and aligning large language models.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

Sources and credits

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  1. Noisy Natural Gradient as Variational Inference proceedings.mlr.press. Checked 19 October 2026.
  2. Three Mechanisms of Weight Decay Regularization arxiv.org. Checked 19 October 2026.
  3. Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model papers.nips.cc. Checked 19 October 2026.
  4. Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers arxiv.org. Checked 19 October 2026.
  5. Elon Musk Announces xAI: Who’s On the 12-Man Founding Team? observer.com. Checked 19 October 2026.
  6. The xAI exodus: Two more cofounders leave — and Musk says he's rebuilding aol.co.uk. Checked 19 October 2026.
  7. Guodong Zhang cs.toronto.edu. Checked 19 October 2026.