Academic Appointments


  • Casual - Other Teaching Staff, Continuing Studies and Summer Session

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)

Professional Education


  • 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)

    Location

    Cambridge, MA

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

    Location

    Cupertino, CA

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

    Location

    Cupertino, CA

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

    Location

    San Jose, CA

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

    Location

    San Jose, Ca

All Publications


  • 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

    Abstract

    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