Elizabeth Mieczkowski

PhD Student @ Princeton CS


I am a first-year PhD student advised by Tom Griffiths and Natalia Vélez in the AI & ML area of the Department of Computer Science at Princeton University. I am supported by the Department of Defense National Defense Science and Engineering Graduate Fellowship (NDSEG) and the Gordon Y. S. Wu Fellowship in Engineering.

I study the optimal strategies underlying multi-agent collaboration using theories from distributed computer systems and large-scale human behavioral data. Currently, I am interested in division of labor and how it can be related to parallel versus serial processing. When and how do groups of people parallelize tasks to effectively minimize time and energy usage? How do we maintain global task coherence when dividing subtasks amongst groups?

Before starting my PhD, I spent two years as a lab tech with Nancy Kanwisher at MIT, where I studied similarities and divergences between CNNs and visual representations in the human brain. I received my B.A. in Computer Science with a minor in Psychology from Cornell University in 2021, where I conducted research in autonomous navigation and natural language processing. In 2019 and 2020, I was a software engineering intern at The New York Times.

Google Scholar // CV

selected publications


  1. fROI-level computational models enable broad-scale experimental testing and expose key divergences between models and brains
    Elizabeth Mieczkowski, Alex Abate, Willian De Faria, and 4 more authors
    Journal of Vision, Aug 2023


  1. Computational Models Recapitulate Key Signatures of Face, Body and Scene Processing in the FFA, EBA, and PPA
    Alex Abate, Elizabeth Mieczkowski, Meenakshi Khosla, and 3 more authors
    Journal of Vision, Aug 2022