PeopleofSuperintelligence

Researcher

Aran Nayebi

Aran Nayebi is a machine learning researcher at Carnegie Mellon University who uses AI models to study how animals see, learn and act. He earned degrees in mathematics and computer science at Stanford and completed a neuroscience PhD there in 2022. At MIT, he led a 2023 study of models that predicted future events in videos and reproduced aspects of animal brain activity during mental simulation. He coauthored work on autonomous virtual zebrafish in 2025, and his 2026 AAAI paper examined mathematical limits on human–AI alignment. He leads the NeuroAgents Lab at Carnegie Mellon, where he is an assistant professor in the Machine Learning Department.

Works
73
Citations
1,710
h-index
17

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. 2015–2017

    Stanford mathematics and computer science

    He earns a BS in mathematics at Stanford University in 2015 and an MS in computer science there in 2017.

  2. 2018

    Recurrent models of vision

    In 2018, he coauthors Task-Driven Convolutional Recurrent Models of the Visual System. The team searches thousands of network designs to identify recurrent cells and feedback connections that help recognize objects.

  3. 2020

    Identifying neural learning rules

    In 2020, he coauthors Identifying Learning Rules From Neural Network Observables. The study trains classifiers on simulated neural network measurements to distinguish learning rules and guide experiments on how brains learn.

  4. March 2022

    Neuroscience PhD at Stanford

    He completes a PhD in Stanford University’s Neurosciences Graduate Program in March 2022, co-advised by Dan Yamins and Surya Ganguli.

  5. 2023

    Mental simulation at MIT

    As a postdoctoral researcher in MIT’s K. Lisa Yang Integrative Computational Neuroscience Center, he leads a study presented at NeurIPS in 2023. The team trains models to predict future states from videos and finds that their activity resembles animal brain activity in Mental-Pong, a task that requires tracking a hidden ball.

  6. 2024

    Career award and CMU appointment

    In 2024, he receives a Burroughs Wellcome Fund Career Award at the Scientific Interface for “Building Integrative, Embodied Agents to Understand Natural Cognition.” He joins Carnegie Mellon University’s Machine Learning Department as an assistant professor in 2024.

  7. 2025

    Autonomous agents from virtual zebrafish

    In 2025, he coauthors Intrinsic Goals for Autonomous Agents: Model-Based Exploration in Virtual Zebrafish Predicts Ethological Behavior and Whole-Brain Dynamics. The team’s 3M-Progress method uses an internal drive to explore, producing virtual zebrafish that predict patterns of animal behavior and brain activity.

  8. 2026

    Mathematical limits on AI alignment

    His paper Intrinsic Barriers and Practical Pathways for Human-AI Alignment: An Agreement-Based Complexity Analysis appears at AAAI in 2026. It treats alignment as agreement among agents over multiple objectives and proves limits on the communication needed as the number of agents or objectives grows.

  9. As of October 2026

    NeuroAgents at Carnegie Mellon

    As of October 2026, he leads Carnegie Mellon’s NeuroAgents Lab, which studies animal intelligence and develops AI systems that learn and adapt. The lab also studies how to keep autonomous AI under human control. He is an assistant professor in the Machine Learning Department, a core faculty member of the Neuroscience Institute, and holds a courtesy appointment in the Robotics Institute.

Career

  1. Since 2024Carnegie Mellon UniversityAssistant Professor, Lab Lead, NeuroAgents Lab
  2. 2022–2024Massachusetts Institute of TechnologyPostdoctoral Researcher

Education

  1. 2022Stanford UniversityPhD
  2. 2017Stanford UniversityMS
  3. 2015Stanford UniversityBS

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

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Aran Nayebi | MetaConscious Group metaconscious.org. Checked 27 October 2026.
  2. [1807.00053] Task-Driven Convolutional Recurrent Models of the Visual System arxiv.org. Checked 27 October 2026.
  3. [2010.11765] Identifying Learning Rules From Neural Network Observables arxiv.org. Checked 27 October 2026.
  4. People - NeuroAILab web.stanford.edu. Checked 27 October 2026.
  5. About Me | Aran Nayebi anayebi.github.io. Checked 27 October 2026.
  6. The brain may learn about the world the same way some computational models do | MIT News | Massachusetts Institute of Technology news.mit.edu. Checked 27 October 2026.
  7. Announcing the 2024 Career Awards at the Scientific Interface Recipients - Burroughs Wellcome Fund bwfund.org. Checked 27 October 2026.
  8. School of Computer Science < Carnegie Mellon University coursecatalog.web.cmu.edu. Checked 27 October 2026.
  9. [2506.00138] Intrinsic Goals for Autonomous Agents: Model-Based Exploration in Virtual Zebrafish Predicts Ethological Behavior and Whole-Brain Dynamics arxiv.org. Checked 27 October 2026.
  10. Research | Aran Nayebi anayebi.github.io. Checked 27 October 2026.
  11. [2502.05934] Intrinsic Barriers and Practical Pathways for Human-AI Alignment: An Agreement-Based Complexity Analysis arxiv.org. Checked 27 October 2026.
  12. CMU Builds on Its Strengths To Advance NeuroAI | Mellon College of Science cmu.edu. Checked 27 October 2026.