About Me

I am a builder passionate about advancing human-AI collaboration to improve our productivity and creativity. I love studying how people work and building solutions to help them work better. I am currently a machine learning engineer at Adobe, where I build and evaluate agentic systems for data analytics products.


I completed my Ph.D. in Computer Science at Northwestern University, advised by Matthew Kay. Back in my Ph.D. days, I built adaptive, scalable, and human-centered AI systems for data visualization and education. I also worked across fields and institutions to explore my interdisciplinary interests: I co-directed EAAMO Bridges; I built mortality estimation models at the Max Planck Institute for Demographic Research; I developed statistical methods for disparity estimation at Stanford's RegLab; I built machine learning solutions for the 988 Lifeline as a Data Science for Social Good fellow at Carnegie Mellon University.

Experience

  • Machine Learning Engineer 2026 – present
    Adobe Inc. · Customer Experience Orchestration
    Designing and developing agentic systems for Adobe Customer Journey Analytics (B2B product).
    Project (coming soon)
  • Ph.D. Researcher 2020 – 2025
    Northwestern University
    Designed and developed adaptive, scalable, and human-centered AI systems for data visualization and education. Published 7 full papers at top-tier HCI and data visualization conferences.
    Publications
  • Machine Learning Engineer Intern 2025
    Adobe Inc. · Digital Experience
    Developed an AI-based data storytelling solution for Adobe Customer Journey Analytics. Intern project went to production, was demoed to the CEO, and is filed for patent.
    Project (coming soon)
  • Visiting Ph.D. Researcher 2024 – 2025
    University of Maryland · FIGX Lab
    Developed an interactive AI-powered system for educational assessment authoring. Led a team of 10+ undergraduate students. Full paper accepted to CHI 2026.
    Ripplet (coming soon)
  • Social Data Science Researcher 2024
    Max Planck Institute for Demographic Research
    Built statistical models to estimate age-specific mortality in a data-scarce context. Coauthored a paper accepted to PAA 2025.
    PAA Poster
  • Graduate Fellow 2023
    Stanford University · RegLab
    Designed statistical sampling techniques to estimate racial disparity when data is scarce. Analyzed a healthcare dataset with ~7M Americans' records.
    Project (coming soon)
  • Data Science Fellow 2022
    Carnegie Mellon University · DSSG
    Built a machine learning system to improve call routing of the 988 Lifeline, which serves ~2M callers per year.
    Code
  • Research Intern 2022
    University of Chicago · Consortium on School Research
    Built statistical models on Chicago Public Schools data to predict students' graduation rates.
  • Undergraduate Researcher 2019
    University of Maryland · REU
    Developed a machine learning model for advance healthcare directives with active learning algorithms.
    Paper

Selected Publications

  • Codesigning Ripplet: an LLM-Assisted Assessment Authoring System Grounded in a Conceptual Model of Teachers' Workflows
    Yuan Cui, Annabel Goldman, Jovy Zhou, Xiaolin Liu, Clarissa Shieh, Joshua Yao, Mia Cheng, Matthew Kay, Fumeng Yang
    ACM CHI ACM Conference on Human Factors in Computing Systems (CHI), 2026
    Paper Demo Software
  • AVEC: An Assessment of Visual Encoding Ability in Visualization Construction
    Lily W. Ge, Yuan Cui, Matthew Kay
    ACM CHI ACM Conference on Human Factors in Computing Systems (CHI), 2025
    Paper
  • Promises and Pitfalls: Using Large Language Models to Generate Visualization Items
    Yuan Cui, Lily W. Ge, Yiren Ding, Lane Harrison, Fumeng Yang, Matthew Kay
    IEEE VIS IEEE Visualization Conference (VIS), 2024
    Paper
  • Adaptive Assessment of Visualization Literacy
    Yuan Cui, Lily W. Ge, Yiren Ding, Fumeng Yang, Lane Harrison, Matthew Kay
    IEEE VIS IEEE Visualization Conference (VIS), 2023
    Paper Demo Code
  • CALVI: Critical Thinking Assessment for Literacy in Visualizations
    Lily W. Ge, Yuan Cui, Matthew Kay
    ACM CHI ACM Conference on Human Factors in Computing Systems (CHI), 2023
    Paper

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