PeopleofSuperintelligence

Researcher

Ruth Fong

Ruth Fong is a computer scientist whose research connects interpretable computer vision with human-centered AI. At Harvard, she combined neuroscience and machine learning, won a Rhodes Scholarship, and continued her studies at Oxford. There, she earned an engineering-science DPhil and coauthored Meaningful Perturbation, a method for explaining black-box classifiers. She joined Princeton's computer-science teaching faculty in 2021 and researches machine-learning fairness, explainability, and how people use AI explanations. In a 2026 study she coauthored, AI-assisted drafts raised feedback provision by 10.8 percentage points in an upper-level machine-learning course; she is a Princeton lecturer and leads the Looking Glass Lab.

Works
30
Citations
1,392
h-index
16

Publication numbers from OpenAlex, as of 27 October 2026.

Timeline

  1. By 2014

    Computer vision at Harvard

    By 2014, she researches how neuroscience and machine learning can advance computer vision in David Cox's Harvard lab.

  2. 2015

    A Rhodes Scholarship

    She becomes one of Harvard's two Rhodes Scholars for 2015 and plans to begin two master's degrees at Oxford in fall 2015.

  3. 2017

    Meaningful Perturbation

    In 2017, she and Andrea Vedaldi publish Meaningful Perturbation, a general framework for learning explanations for black-box algorithms and finding image regions most responsible for classifier decisions.

  4. 2018

    Open Philanthropy AI Fellowship

    While a DPhil student in Oxford's Engineering Science department, she becomes one of seven researchers worldwide selected for the 2018 Open Philanthropy AI Fellowship.

  5. 2020

    Neuroscience and DPhil at Oxford

    At Oxford, she earns a master's in neuroscience and completes a DPhil in engineering science in 2020; Andrea Vedaldi advises her in the Visual Geometry Group.

  6. 2020

    Verifiable claims for trustworthy AI

    She coauthors the 2020 report Toward Trustworthy AI Development, which analyzes ten mechanisms to support verifiable claims about AI safety, security, fairness, and privacy.

  7. 2021–2022

    Teaching and research at Princeton

    She joins Princeton's computer-science teaching faculty in 2021. In 2022, the department's teaching-plus-research model gives her time to study machine-learning fairness and create accessible explanations of complex AI models.

  8. 2023

    Explainability with end users

    In 2023, she coauthors “Help Me Help the AI,” a mixed-methods study with 20 users of the Merlin bird-identification app that examines their needs, uses, and perceptions of AI explanations. The paper receives a CHI honorable mention.

  9. 2026

    AI-assisted feedback in education

    In 2026, she coauthors a mixed-methods study of an upper-level machine-learning course with 11 TAs and 88 students. The randomized field experiment finds that AI-assisted drafts raise feedback provision by 10.8 percentage points.

  10. Since 2021

    Princeton lecturer and lab lead

    As of October 2026, she is a lecturer in Princeton's Department of Computer Science and leads the Looking Glass Lab; her research spans human-centered AI, machine learning, computer vision, and human-computer interaction.

Career

  1. NowPrinceton UniversityLecturer, Lead, Looking Glass Lab

Education

  1. 2020University of OxfordDPhil in Engineering Science
  2. University of OxfordMaster's in Neuroscience

From the biography.

Checked career sources: eng.ox.ac.uk, cs.princeton.edu, ruthfong.com, cs.princeton.edu, engineering.princeton.edu.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. RUTH FONG ’15 NAMED RHODES SCHOLAR mcb.harvard.edu. Checked 27 October 2026.
  2. Interpretable Explanations of Black Boxes by Meaningful Perturbation arxiv.org. Checked 27 October 2026.
  3. DPhil student Ruth Fong selected as AI Fellow eng.ox.ac.uk. Checked 27 October 2026.
  4. Ruth Fong cs.princeton.edu. Checked 27 October 2026.
  5. Ruth Fong ruthfong.com. Checked 27 October 2026.
  6. Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims arxiv.org. Checked 27 October 2026.
  7. Ruth Fong: Teaching the creativity of computer science cs.princeton.edu. Checked 27 October 2026.
  8. "Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction arxiv.org. Checked 27 October 2026.
  9. AI Assistance for Discretionary Work: Increasing Feedback Provision in Higher Education arxiv.org. Checked 27 October 2026.
  10. Ruth Fong - Princeton Engineering engineering.princeton.edu. Checked 27 October 2026.