About

Jillian Fisher

Postdoctoral Scholar, Communication Department, Stanford University

  • (2026–present) Postdoctoral Scholar, Communication Department, Stanford University
  • (2026) Postdoctoral Scholar, Paul G. Allen School of CSE, University of Washington
  • (2025) Ph.D. Statistics (with CSE), University of Washington
    Dissertation: Statistical Methods Towards Trustworthy AI: From Diagnosis to Control and Societal Implications. Advisors: Yejin Choi (CSE) and Thomas Richardson (Statistics)
  • (2019) M.S. Statistics, Texas A&M
  • (2015) B.A. Mathematics and Psychology, University of Texas

Research Statement: My research examines how AI systems affect the people and society that rely on them. I am interested in how AI shapes what people know, believe, and decide; in developing rigorous methods to measure those effects; and in designing safeguards that protect users and help AI systems serve diverse people well. Drawing on my training in statistics, I combine empirical studies, evaluation methodology, and technical methods, and I approach these questions through an interdisciplinary lens informed by NLP, communication, political science, and psychology.

Awards and Grants

  • NSF ACCESS Discover Allocation (Principal Investigator) – approximately $40,000 in CloudBank compute credits for research on political bias in multi-agent LLM systems, 2026
  • Honorable Mention, ASA Statistical Learning and Data Science Student Paper Award, 2023

Industry Experience

  • Forum AI, Remote – Research Consultant (January – June 2026)
    Advised on evaluation design for NewsBench, a benchmark of epistemic quality in AI political news, and co-authored the NewsBench white paper.
  • Microsoft Research, Redmond, WA – Research Science Intern (May – August 2025)
    Designed and led a project developing a Bayesian-optimal memory selection method for LLM personalization, resulting in a first-author paper at EMNLP 2026.
  • Meta, New York, NY – Data Science Intern (June – September 2024)
    Developed a metric to assess the “contextuality” of ads in Instagram's Multi-ads format, which was integrated into the ads pipeline, and co-designed a large (>2K) human study evaluating ad contextuality.
  • Allen Institute for AI (AI2), Seattle, WA – Research Intern (June – September 2022)
    Led a project to improve models' ability to construct more human-aligned advice, including a 500+ participant user study.
  • Amazon AWS, Seattle, WA – Data Science Intern (June 2021 – June 2022)
    Doubled the accuracy of the AWS hiring forecast and analyzed 1.5M data points on demographic diversity and market factors to drive 2022 hiring goals.

Professional Service

  • Institute for Foundations of Machine Learning (IFML) Workshop, 2023 (lead organizer)
  • Conference Reviewer: ICML, ICLR, ACL, EMNLP, NAACL
  • Workshop Reviewer: NLP for Democracies (MultiDems), 2026; Pluralistic Alignment Workshop, 2026
  • Pre-Application Review Service (PARS), UW Statistics, 2023 – 2024
  • Department of Statistics Outreach and Community Committee (SOCC), UW Statistics, 2021 – 2025
  • Statisticians and Biostatisticians of Underrepresented Genders (SBUG) member, UW Statistics, 2022 – 2025