HKUST Visualization and Artificial Intelligence Lab

The VisAI Lab studies at the intersection of visualization and artificial intelligence, with special emphasis on biomedical applications.

Research Themes

Data Visualization
We design interactive visualizations that help researchers explore complex biomedical data, generate hypotheses, and make discoveries across genomics, health, and biology.
AI Interpretability
We develop visual analytics methods that help scientists understand, inspect, and reason about AI models, with an emphasis on biomedical research workflows.
Toolkits for Scientists
We build scalable systems, grammars, and authoring tools that help scientists create visualizations and interact with AI in expressive, reusable, and accessible ways.
Biomedical Applications
We apply visualization and AI methods to biomedical science, education, and healthcare, working with domain experts to address real-world data challenges.

Selected Papers [see more]

Automatic Synthesis of Visualization Design Knowledge Bases
Hyeok Kim, Sehi L'Yi, Nils Gehlenborg, Jeffrey Heer
In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI ’26) (2026)
25.3% acceptance rate
Learnable and Expressive Visualization Authoring Through Blended Interfaces
Sehi L'Yi, Astrid van den Brandt, Etowah Adams, Huyen N. Nguyen, Nils Gehlenborg
IEEE Transactions on Visualization and Computer Graphics (TVCG) (Proc. VIS) (2025)
23.2% acceptance rate
Best Paper Honorable Mention
Chromoscope: interactive multiscale visualization for structural variation in human genomes
Sehi L'Yi, Dominika Maziec, Victoria Stevens, Trevor Manz, Alexander Veit, Michele Berselli, Peter J Park, Dominik Głodzik, Nils Gehlenborg
Nature Methods (2023) 20, 1834–1835
Innovation In Cancer Informatics Major Grant Award 2023
GenoREC: A Recommendation System for Interactive Genomics Data Visualization
Aditeya Pandey**, Sehi L'Yi, Qianwen Wang, Michelle A Borkin, Nils Gehlenborg
IEEE Transactions on Visualization and Computer Graphics (TVCG) (Proc. VIS) (2023) 29(1), 570-580
26.5% acceptance rate
IEEE InfoVis Best Poster Research Award
Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization
Sehi L'Yi, Qianwen Wang, Fritz Lekschas, Nils Gehlenborg
IEEE Transactions on Visualization and Computer Graphics (TVCG) (Proc. VIS) (2022) 28(1), 40-150
25.8% acceptance rate
ISMB/ECCB BioVis Best Abstract Award

Media Coverage

Nature (TECHNOLOGY FEATURE)
A graphics toolkit for visualizing genome data
Powerful 'grammar' allows geneticists to display their data in interactive and scalable illustrations.