PeopleofSuperintelligence

Researcher

Maggie Henderson

Also known as Margaret M. Henderson

Maggie Henderson is a computational neuroscientist who uses neural networks and brain imaging to study human vision. She studied biological sciences at Cornell University and earned her neuroscience Ph.D. at the University of California, San Diego in 2021, working with John Serences. Her work included BrainDiVE, which used diffusion models to generate images predicted to activate visual brain regions, and BrainSCUBA, which generated language descriptions of visual cortex selectivity. In 2025, she coauthored BraInCoRL, a transformer model that predicted brain responses from a few examples for new participants. She is an assistant professor in Carnegie Mellon University's Psychology Department and Neuroscience Institute, with an affiliated appointment in the Machine Learning Department.

Timeline

  1. 2021

    A doctorate at UC San Diego

    After earning a B.S. in Biological Sciences at Cornell University, she completes her Ph.D. in Neurosciences at the University of California, San Diego in 2021, with a specialization in computational neuroscience. She works with John Serences.

  2. 2023

    BrainDiVE generates images

    In 2023, she coauthors Brain Diffusion for Visual Exploration with Andrew F. Luo, Leila Wehbe and Michael J. Tarr. Their BrainDiVE method combines diffusion models with brain-guided image generation to produce images predicted to activate particular visual brain regions, using natural images paired with fMRI recordings.

  3. 2024

    A faculty position at CMU

    Following postdoctoral work at Carnegie Mellon University with Leila Wehbe and Michael Tarr, Henderson joins its Psychology Department and Neuroscience Institute as a faculty member in 2024.

  4. 2024

    BrainSCUBA describes visual selectivity

    She coauthors BrainSCUBA, presented at the International Conference on Learning Representations in 2024. The method combines a contrastive vision-language model with a pretrained large language model to describe images predicted to activate individual locations in visual cortex.

  5. April 2025

    Categorization changes visual representations

    In April 2025, she publishes Dynamic categorization rules alter representations in human visual cortex with John T. Serences and Nuttida Rungratsameetaweemana. Using fMRI during a shape categorization task, the team finds that visual cortex representations change with the current decision boundary, with these changes associated with better task performance.

  6. 2025

    BraInCoRL predicts brain responses

    In 2025, she coauthors Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex. Its BraInCoRL model uses a few example images and brain responses to predict responses for new participants and images without additional fine-tuning.

  7. July 2026

    Neural networks model peripheral vision

    With Dylan M. Diaz, she publishes Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field in July 2026. They train ResNet-18 models on video frames selected using eye-tracking data, then compare the models with human fMRI responses to study how central and peripheral visual experience shapes representations.

  8. As of October 2026

    Visual cognition at Carnegie Mellon

    In 2026, she works as an assistant professor in Carnegie Mellon University's Psychology Department and Neuroscience Institute, with an affiliated assistant professorship in the Machine Learning Department. Her team collects fMRI data and builds models that predict how different areas of human visual cortex respond to images.

Sources and credits

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  1. People | Henderson Lab hendersonneurolab.com. Checked 27 October 2026.
  2. Computational Neuroscience Specialization neurograd.ucsd.edu. Checked 27 October 2026.
  3. Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models arxiv.org. Checked 27 October 2026.
  4. tarrlab - home sites.google.com. Checked 27 October 2026.
  5. BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity arxiv.org. Checked 27 October 2026.
  6. Dynamic categorization rules alter representations in human visual cortex nature.com. Checked 27 October 2026.
  7. Meta-Learning an In-Context Transformer Model of Human Higher Visual Cortex arxiv.org. Checked 27 October 2026.
  8. Eccentricity-Constrained CNN Training Reveals Adaptive Information Coding Around the Visual Field arxiv.org. Checked 27 October 2026.
  9. Maggie Henderson - Department of Psychology - Dietrich College of Humanities and Social Sciences - Carnegie Mellon University cmu.edu. Checked 27 October 2026.
  10. CMU Builds on Its Strengths To Advance NeuroAI | Neuroscience Institute ni.pantheon.cmu.edu. Checked 27 October 2026.