PeopleofSuperintelligence

Researcher

Daniel Yamins

Also known as Dan Yamins

Daniel Yamins is a Stanford researcher whose work uses artificial neural networks to study vision, hearing and the brain’s algorithms. He completed an applied mathematics PhD at Harvard in 2008 and joined Stanford in September 2016 after postdoctoral work in MIT’s Department of Brain and Cognitive Sciences. He coauthored work on automated hyperparameter search, human auditory perception and ThreeDWorld, a platform for simulating physical interactions. His 2013 model-search paper with James Bergstra and David Cox became a runner-up for ICML’s 2023 Test-of-Time Award. As of October 2026, he is an associate professor of psychology and computer science and leads the Stanford NeuroAILab, which connects models of the brain with AI development.

Works
186
Citations
14,436
h-index
36

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2008

    A Harvard mathematics doctorate

    He completes a PhD in applied mathematics at Harvard University in 2008.

  2. 2013

    Making a science of model search

    In 2013, he coauthors “Making a Science of Model Search” with James Bergstra and David Cox at ICML. The paper presents a method for automatically choosing hyperparameters, the settings that control machine-learning algorithms, and tests it on face and object recognition.

  3. 2014

    Neural networks model object recognition

    In 2014, he coauthors “Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition,” comparing neural networks with object representations in the primate visual cortex. The team finds that the best network in its comparison classifies objects as accurately as the macaque brain.

  4. September 2016

    Joining Stanford from MIT

    He joins Stanford in September 2016 after postdoctoral work in MIT’s Department of Brain and Cognitive Sciences.

  5. April 2018

    A model of human hearing

    In April 2018, he and Alexander Kell are lead authors of a Neuron study that trains a deep neural network to identify spoken words and musical genres. The model matches human listeners’ accuracy and tends to make mistakes on the same sound clips; its processing stages also help explain the hierarchy of the auditory cortex.

  6. 2019

    Learning visual features without labels

    With Chengxu Zhuang and Alex Lin Zhai, he coauthors “Local Aggregation for Unsupervised Learning of Visual Embeddings” at ICCV in 2019. The method trains a network to group similar images in its learned representation and tests the resulting features on object recognition, scene recognition and object detection.

  7. 2021

    ThreeDWorld for physical simulation

    In 2021, he coauthors the ThreeDWorld paper in the NeurIPS Datasets and Benchmarks proceedings. The platform combines images, sound and physical interactions in simulated 3D environments, supporting experiments with AI agents and human participants using virtual reality.

  8. 2023

    An ICML Test-of-Time runner-up

    In 2023, “Making a Science of Model Search,” his 2013 paper with James Bergstra and David Cox, is one of two runners-up for ICML’s Test-of-Time Award. The recognition highlights its experiments in automatically tuning hundreds of hyperparameters.

  9. May 2024

    Mapping the visual cortex

    In May 2024, a Stanford team led by Yamins and Kalanit Grill-Spector presents a topographic deep artificial neural network in Neuron. By combining visual learning with constraints on nearby artificial neurons, the model predicts both responses to images and the spatial organization of parts of the human visual system.

  10. As of October 2026

    Associate professor and NeuroAILab head

    As of October 2026, he is an associate professor in Stanford’s Departments of Psychology and Computer Science and leads the Stanford NeuroAILab. His group studies the brain’s algorithms to understand the mind and build more effective AI systems.

Career

  1. NowStanford UniversityAssociate Professor of Psychology and Computer Science
  2. NowStanford UniversityHead of Stanford NeuroAILab
  3. From 2016Stanford University
  4. Massachusetts Institute of TechnologyPostdoctoral Researcher in the Department of Brain and Cognitive Sciences

Education

  1. 2008Harvard UniversityPhD in applied mathematics
  2. Harvard UniversityBA in Mathematics

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

Checked career sources: web.stanford.edu.

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 Daniel Yamins yet. Send us a free-licensed photo.

  1. Daniel Yamins | Computer Science cs.stanford.edu. Checked 27 October 2026.
  2. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures proceedings.mlr.press. Checked 27 October 2026.
  3. In one aspect of vision, computers catch up to primate brain news.mit.edu. Checked 27 October 2026.
  4. People - NeuroAILab web.stanford.edu. Checked 27 October 2026.
  5. Machine-learning system processes sounds like humans do news.mit.edu. Checked 27 October 2026.
  6. Local Aggregation for Unsupervised Learning of Visual Embeddings openaccess.thecvf.com. Checked 27 October 2026.
  7. ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation datasets-benchmarks-proceedings.neurips.cc. Checked 27 October 2026.
  8. Test-of-Time Award icml.cc. Checked 27 October 2026.
  9. Neuroscientists use AI to simulate how the brain makes sense of the visual world neuroscience.stanford.edu. Checked 27 October 2026.
  10. Dan Yamins - Home stanford.edu. Checked 27 October 2026.