Honors & Awards

  • Inaugural HAI Fellow, Human-Centered Artificial Intelligence Institute (06/2021)
  • VPGE Fellow, Office of the Vice Provost for Graduate Education Stanford University (04/2021)
  • GEM Fellow, National GEM Consortium (06/2016)
  • CAMP Scholar, National Science Foundation (06/2014)

Education & Certifications

  • MS, Stanford University, Computational and Mathematical Engineering (2018)
  • BS, University of California, San Diego, Mathematics (2016)
  • BS, University of California, San Diego, Engineering Sciences (2016)

Work Experience

  • Technical Associate, MIT (June 2019 - September 2021)


    Cambridge, MA

  • Machine Learning Intern, Apple (January 2019 - June 2019)


    Cupertino, CA

  • Machine Learning Intern, Apple (June 2018 - September 2018)


    Cupertino, CA

  • Video and Data Research Intern, Adobe (June 2017 - September 2017)


    San Jose, CA

  • Video and Data Research Intern, Adobe (June 2016 - October 1, 2016)


    San Jose, Ca

All Publications

  • Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition. Proceedings of the National Academy of Sciences of the United States of America Dobs, K., Yuan, J., Martinez, J., Kanwisher, N. 2023; 120 (32): e2220642120


    Human face recognition is highly accurate and exhibits a number of distinctive and well-documented behavioral "signatures" such as the use of a characteristic representational space, the disproportionate performance cost when stimuli are presented upside down, and the drop in accuracy for faces from races the participant is less familiar with. These and other phenomena have long been taken as evidence that face recognition is "special". But why does human face perception exhibit these properties in the first place? Here, we use deep convolutional neural networks (CNNs) to test the hypothesis that all of these signatures of human face perception result from optimization for the task of face recognition. Indeed, as predicted by this hypothesis, these phenomena are all found in CNNs trained on face recognition, but not in CNNs trained on object recognition, even when additionally trained to detect faces while matching the amount of face experience. To test whether these signatures are in principle specific to faces, we optimized a CNN on car discrimination and tested it on upright and inverted car images. As we found for face perception, the car-trained network showed a drop in performance for inverted vs. upright cars. Similarly, CNNs trained on inverted faces produced an inverted face inversion effect. These findings show that the behavioral signatures of human face perception reflect and are well explained as the result of optimization for the task of face recognition, and that the nature of the computations underlying this task may not be so special after all.

    View details for DOI 10.1073/pnas.2220642120

    View details for PubMedID 37523537

  • Brain-like functional specialization emerges spontaneously in deep neural networks. Science advances Dobs, K., Martinez, J., Kell, A. J., Kanwisher, N. 2022; 8 (11): eabl8913


    The human brain contains multiple regions with distinct, often highly specialized functions, from recognizing faces to understanding language to thinking about what others are thinking. However, it remains unclear why the cortex exhibits this high degree of functional specialization in the first place. Here, we consider the case of face perception using artificial neural networks to test the hypothesis that functional segregation of face recognition in the brain reflects a computational optimization for the broader problem of visual recognition of faces and other visual categories. We find that networks trained on object recognition perform poorly on face recognition and vice versa and that networks optimized for both tasks spontaneously segregate themselves into separate systems for faces and objects. We then show functional segregation to varying degrees for other visual categories, revealing a widespread tendency for optimization (without built-in task-specific inductive biases) to lead to functional specialization in machines and, we conjecture, also brains.

    View details for DOI 10.1126/sciadv.abl8913

    View details for PubMedID 35294241