Qianwen Wang

Qianwen Wang

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

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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).

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

My studies combine interactive visualization with interpretable machine learning to foster intuitive, efficient, and reliable Human-AI collaboration. My research contributes to innovative visualization techniques, Human-AI interfaces, and their practical applications (e.g., biomedical knowledge graphs, single-cell omics).

My research has made contributions to visualization, human-computer interaction, and bioinformatics, as demonstrated two honorable mention from IEEE VIS (2022, 2024), 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, including as General Chair for ISMB BioVis, VisNotes and Poster Chair for IEEE PacificVis, and as a Program Committee member for both IEEE VIS, ACM CHI, and ACM IUI.

News

  • [Oct, 2024] We will present KnowNet (full paper) and GNN101 (VISxAI workshop) at IEEE VIS 2024. KnowNet also won a Best Paper Honorable Mention award 🏆
  • [Oct, 2024] TxGNN was accepted by Nature Medicine, and reported by Harvard Gazette and Axios.
  • [Jan, 2024] I am honored to serve as a General Chair for BioVis@ISMB 2024
  • [Oct, 2023] Two papers, one workshop paper, and one VIS4ML panel at IEEE VIS 2023. First time in Australia!
  • [Aug, 2023] I am honored to be invited as a speaker for a Kavli Frontiers of Science Symposium 🎉
  • [Jun, 2023] I am honored to serve as a VisNotes chair for PacificVis 2024 🎉
  • [Jan, 2023] I am honored to continue serving as Abstract Chair for BioVis@ISMB 2023
  • [Jul, 2022] Drava are accepted by ACM CHI 2023 🎉
  • [Jul, 2022] Our XAI design study won a Hornorable Mention Award 🏆 at IEEE VIS 2022
  • [Jul, 2022] Polyphony won the Best Abstract Award 🏆 at ISMB 2022
  • [Jul, 2022] I am honored to serve as a poster chair for PacificVis 2023 🎉
  • [Jul, 2022] Three papers (PolyPhony, GenoRec, and a design study on XAI ) are accepted by IEEE VIS 2022 🎉
  • [Jun, 2022] I am hornored to receive the Postdoctoral Fellows Research Fund Award 🏆 from Harvard Data Science Initiative
  • [Apr, 2022] We will host a Visualization in Biomedical AI workshop at IEEE VIS 2022 🎉
  • [Feb, 2022] GNNLens is accepted by IEEE TVCG
  • [Feb, 2022] We will host a Gosling/Gos tutorial BioVis@ISMB2022 🎉
  • [Jan, 2022] I am honored to serve as Abstract Chair for BioVis@ISMB2022
  • [Jul, 2021] Gosling won the Best Abstract Award 🏆 at ISMB 2021
  • [Jul, 2021] Two papers accepted by IEEE VIS
  • [Jul, 2021] One paper accepted by ICML workshop on Interpretable Machine Learning in Healthcare (🏆 Best Paper Award)
  • [Jul, 2021] Two long talks at ISMB 2021
  • [Mar, 2021] OncoThread is aceepted by ISMB Proceeding track
  • [Apr, 2021] Invited talk at PacificVis 2021 Visualization meets AI
  • [Jul, 2020] Three papers accepted by IEEE VIS
  • [May, 2020] Started my PostDoc position at Harvard University
  • [Dec, 2019] Passed my PhD thesis defense 🎉
  • [Aug-Dec, 2019] Visited the University of Oxford, hosted by Prof. Min Chen

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).