Xinyi Liu

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I am a second-year PhD student in Computer Science at Northeastern University, advised by Prof. David A. Smith.

My research focuses on evaluating and aligning LLMs and AI agents, particularly how their reasoning, behavior, and interactions with humans remain consistent with human goals and agency. I am broadly interested in LLM/agent evaluation, alignment and AI safety, structured reasoning and information extraction, and human–AI interaction.

Previously, I conducted research at Microsoft Research in the Social Computing group and at Harvard Medical School in the HIDIVE Lab, where I was advised by Prof. Nils Gehlenborg. I received my M.S. from The University of Texas at Austin and my B.S. in Computer Science and Economics from the University of Maryland, College Park, where I worked with Prof. Zhicheng Liu and Prof. Hannah Bako.

Current Research

01
LLM–Agent Consistency
Do models preserve their judgments as they move from understanding a situation to responding and acting?
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02
Structured Language Understanding
Can language models recover complex relations and their interdependencies from natural language?
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03
Human-Centered AI Alignment
How can AI systems support human goals, wellbeing, and agency over time?
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Open to Collaboration

Our lab welcomes visiting students and remote undergraduate and master’s researchers. I also welcome collaborations with PhD students and researchers in academia and industry.

If you are interested in joining, collaborating, or discussing a research idea, feel free to reach out by email.

news

Sep 01, 2026 Serving as a Teaching Assistant for CS 7170: Seminar in AI at Northeastern University for Fall 2026.
Jan 01, 2026 Our paper, “Can LLMs bridge domain and visualization? A case study on high-dimension data visualization in single-cell transcriptomics,” was published in IEEE TVCG.
Nov 02, 2025 Served as Technology Chair on the Organizing Committee of IEEE VIS 2025.
Jan 05, 2025 Served as an Associate Chair for ACM Creativity & Cognition 2025.
Nov 30, 2024 Awarded as a Super Reviewer for ACM CHI 2025.

selected publications

  1. HD.png
    Can LLMs Bridge Domain and Visualization? A Case Study on High-Dimension Data Visualization in Single-Cell Transcriptomics
    Qianwen Wang* , Xinyi Liu* , and Nils Gehlenborg
    IEEE Transactions on Visualization and Computer Graphics, 2026
  2. examples2.png
    Unveiling how examples shape visualization design outcomes
    Hannah K Bako , Xinyi Liu , Grace Ko , Hyemi Song , Leilani Battle , and Zhicheng Liu
    IEEE Transactions on Visualization and Computer Graphics, 2024
  3. nlp4vis.png
    Evaluating the semantic profiling abilities of llms for natural language utterances in data visualization
    Hannah K Bako , Arshnoor Bhutani , Xinyi Liu , Kwesi A Cobbina , and Zhicheng Liu
    In 2024 IEEE Visualization and Visual Analytics (VIS), 2024
  4. K12_ML.png
    Creating an authoring tool for k-12 teachers to design ml-supported scientific inquiry learning
    Xiaofei Zhou , Jingwan Tang , Hanjia Lyu , Xinyi Liu , Zhenhao Zhang , Lichen Qin , Fiona Au , Advait Sarkar , and Zhen Bai
    In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024
  5. examples1.png
    Understanding how designers find and use data visualization examples
    Hannah K Bako , Xinyi Liu , Leilani Battle , and Zhicheng Liu
    IEEE Transactions on Visualization and Computer Graphics, 2022