Qianwen Wang

Data Visualization
Explainable Machine Learning
Human-Machine Collaboration
Visualization in Biomedical AI

Find me at

About me

I am Qianwen Wang (汪倩雯), a tenure-track Assistant Professor in the CS department at the University of Minnesota, Twin Cities (UMN).

Join our new lab: I am seeking highly motivated students, RAs, and interns to be part of our dynamic team at UMN CS. If you're interested, check out further details on Work with Me

As a visualization researcher, my studies combine interactive visualization with interpretable machine learning to help users better explore, understand, and generate insights from their data. My research explores both innovative visualization techniques and their practical application, with a particular emphasis on addressing biomedical challenges (e.g., patient cohorts, genomics, single-cell omics).

My research has made contributions to visualization, human-computer interaction, and bioinformatics, as demonstrated one honorable mention from IEEE VIS 2022, one best paper award from IMLH@ICML 2021, and two best abstract awards from BioVis ISMB 2021-2022. I am an awardee of the HDSI Postdoctoral Research Fund and my research has been covered by MIT News and Nature Technology Features. I actively contribute to the academic community by serving in multiple roles, including as Abstract Chair for ISMB BioVis, Poster Chair for IEEE PacificVis, and as a Program Committee member for both IEEE VIS and ACM IUI.


Research Themes

Human-AI Collaboration

I design and develop tools to facilitate Human-AI collaboration, which also drives my investigation on visualization techniques, algorithms, and design frameworks.

Automatic & Intelligent Visualization

I propose techniques, authoring tools, and machine learning algorithms in pursuit of making visualizations that can be accurately interpreted and easily used by everyone.

VIS+(X)AI in Biomed/Healthcare

Through wide collaboration, I am studying how VIS + (X)AI can promote scientific discoveries, especially in the field of biomedicine and healthcare (e.g., genomics, single-cell, and cohort analysis).