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Jonathan Ivey

PhD Student at Johns Hopkins University

I am a PhD Student in Computer Science at Johns Hopkins University co-advised by Anjalie Field and Ziang Xiao. Previously, I completed my undergraduate degrees in data science and mathematics at the University of Arkansas where I was advised by Dr. Susan Gauch and collaborated with Dr. David Jurgens at the University of Michigan.

My research interests are currently focused on capturing nuance in text. I am particularly interested in NLP methods for collecting and analyzing qualitative data at scale. Previously, I have done research on retaining disagreement in human annotations, evaluating the ability of LLMs to simulate human dialogues, and helping stress detection models generalize to minorities.

In general, I love projects that try to model complicated, context-dependent data, and I am always looking for creative visualizations. Outside of work, I play guitar and enjoy watching movies. If you have any questions or just want to chat, please reach out!

News

April 2026

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Our new preprint, What Makes a Good Response? An Empirical Analysis of Quality in Qualitative Interviews, is out now.
April 2026

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Our paper, Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialogue, has been accepted to ACL 2026 (Findings)!
August 2025

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I officially began my PhD in Computer Science at Johns Hopkins University!
More News

Selected Publications

  1. InterviewQuality.png
    What Makes a Good Response? An Empirical Analysis of Quality in Qualitative Interviews
    Jonathan Ivey, Anjalie Field, and Ziang Xiao
    Preprint, 2026
  2. RealOrRobotic.png
    Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialogue
    Jonathan Ivey, Shivani Kumar, Jiayu Liu, and 12 more authors
    Findings of the Association for Computational Linguistics: ACL 2026, 2026
  3. NUTMEG.png
    NUTMEG: Separating Signal From Noise in Annotator Disagreement
    Jonathan Ivey, Susan Gauch, and David Jurgens
    Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025