PeopleofSuperintelligence

Researcher

Tim Salimans

Tim Salimans is a machine learning researcher whose work on generative models spans GAN training, evolution strategies and diffusion models. They completed a PhD at Erasmus University Rotterdam in 2013 and then worked as a research scientist at OpenAI, where they coauthored Weight Normalization and Improved Techniques for Training GANs in 2016 and Evolution Strategies as a Scalable Alternative to Reinforcement Learning in 2017. After moving to Google in 2018, they led a team on generative models in Amsterdam, wrote Progressive Distillation for Fast Sampling of Diffusion Models with Jonathan Ho in 2022, and coauthored Imagen, whose paper received a NeurIPS 2022 Outstanding Paper award. At Anthropic, they are a member of technical staff.

Now at
Anthropic
Works
70
Citations
25,836
h-index
34
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2013

    A PhD at Erasmus University

    They complete a PhD at Erasmus University Rotterdam in 2013, with their research in economics done at the Tinbergen Institute. Their dissertation, Essays in Likelihood-Based Computational Econometrics, has Richard Paap and Dennis Fok as advisors.

  2. 2016

    Weight normalization and better GANs

    At OpenAI, where they work as a research scientist from 2016 to 2018, they coauthor Weight Normalization with Diederik Kingma, a reparameterization of network weights that speeds up training. In 2016, they also coauthor Improved Techniques for Training GANs with Ian Goodfellow and four others, which reports state-of-the-art semi-supervised results on MNIST, CIFAR-10 and SVHN.

  3. 2017

    Evolution strategies at scale

    In March 2017, they coauthor Evolution Strategies as a Scalable Alternative to Reinforcement Learning with Jonathan Ho, Xi Chen, Szymon Sidor and Ilya Sutskever. The method scales to over a thousand parallel workers and solves 3D humanoid walking in 10 minutes.

  4. 2018–2019

    Google Brain in Amsterdam

    They move to Google in 2018 and lead a team on generative models in Amsterdam. In December 2019, at Google Brain, they coauthor Axial Attention in Multidimensional Transformers with Jonathan Ho, Nal Kalchbrenner and Dirk Weissenborn, which proposes Axial Transformers, a self-attention-based autoregressive model for images.

  5. 2022

    Progressive distillation for diffusion

    With Jonathan Ho, they post Progressive Distillation for Fast Sampling of Diffusion Models in February 2022, and it appears at ICLR 2022. The method keeps halving the number of sampling steps, distilling samplers that take as many as 8192 steps into models that take as few as 4 steps, and reaches an FID of 3.0 on CIFAR-10.

  6. 2022

    Imagen wins a NeurIPS award

    In May 2022, they are one of 14 authors of Imagen, a text-to-image diffusion model from Google Research's Brain Team that reaches an FID of 7.27 on the COCO dataset. The paper, Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding, receives a NeurIPS 2022 Outstanding Paper award.

  7. February 2026

    Unified Latents at DeepMind

    In February 2026, they coauthor Unified Latents (UL): How to train your latents with Jonathan Heek, Emiel Hoogeboom and Thomas Mensink, all at Google DeepMind Amsterdam. The framework reaches an FID of 1.4 on ImageNet-512 and an FVD of 1.3 on Kinetics-600.

  8. As of October 2026

    Technical staff at Anthropic

    They move from Google DeepMind in Amsterdam to Anthropic, where they are a member of technical staff.

Career

  1. NowAnthropicMember of technical staff
  2. 2026Google DeepMind
  3. 2019–2026Google
  4. 2018–2019Google BrainTeam lead
  5. 2016–2018OpenAIResearch scientist

Education

  1. 2013Erasmus University RotterdamPhD

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. 22 of 133 people shaping AI with a former OpenAI role now work at AnthropicIncludes Tim Salimans. Our estimates of where OpenAI recruits came from and where 133 people with former OpenAI roles work now, including the companies and labs they founded.
  2. The median h-index is 48 at universities and 14 at frontier AI labsIncludes Tim Salimans. 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.
  3. Of 943 people shaping AI who worked at Google, 446 work there nowIncludes Tim Salimans. Our estimates of where the 943 people shaping AI who worked at Google and Google DeepMind work now: 446 at Google and 452 across 278 other organizations.
  4. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes Tim Salimans. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Tim Salimans yet. Send us a free-licensed photo.

  1. Promotie Tim Salimans op donderdag 23 mei 2013 | Erasmus University Rotterdam eur.nl. Checked 20 October 2026.
  2. Tim Salimans - The Mathematics Genealogy Project mathgenealogy.org. Checked 20 October 2026.
  3. Tim Salimans | alphaXiv alphaxiv.org. Checked 20 October 2026.
  4. Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks arxiv.org. Checked 20 October 2026.
  5. Improved Techniques for Training GANs arxiv.org. Checked 20 October 2026.
  6. [1606.03498] Improved Techniques for Training GANs ar5iv.labs.arxiv.org. Checked 20 October 2026.
  7. Evolution Strategies as a Scalable Alternative to Reinforcement Learning arxiv.org. Checked 20 October 2026.
  8. [1912.12180] Axial Attention In Multidimensional Transformers ar5iv.labs.arxiv.org. Checked 20 October 2026.
  9. Axial Attention in Multidimensional Transformers arxiv.org. Checked 20 October 2026.
  10. Progressive Distillation for Fast Sampling of Diffusion Models arxiv.org. Checked 20 October 2026.
  11. Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding arxiv.org. Checked 20 October 2026.
  12. Imagen: Text-to-Image Diffusion Models imagen.research.google. Checked 20 October 2026.
  13. NeurIPS 2022 Awards neurips.cc. Checked 20 October 2026.
  14. Unified Latents (UL): How to train your latents arxiv.org. Checked 20 October 2026.
  15. Tim Salimans - Member of Technical Staff at Anthropic | The Org theorg.com. Checked 20 October 2026.