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Qianwen Wang - IEEE Xplore Author Profile

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Author details

Qianwen Wang

Publications

19

Citations

357

Publications by Year

20192025

Co-Authors:

William AlexanderJohanna BeyerMichelle A. BorkinChuan BuEthan Cerami

Show All Co-Authors (53)

Qianwen Wang

Affiliation

Department of Computer Science and Engineering

University of Minnesota

Twin Cities, MN, USA

Publication Topics

Visual Design,
Domain Experts,
Interactive Visualization,
Visualization Tool,
Data Visualization,
Genomic Data,
Visual Exploration,
Design Process,
Natural Language,
Scatter Plot,
Visual Encoding,
Analysis Of Genomic Data

Biography

Qianwen Wang received the BS degree in electronics engineering from Xi'an Jiaotong University. She is now working toward the PhD degree at Hong Kong University of Science and Technology. Her research interest focus on information visualization and interactive machine learning.(Based on document published on 13 January 2019).

Publications

19

Citations

357

Publications by Year

20192025

Co-Authors:

William Alexander
Johanna Beyer
Michelle A. Borkin
Chuan Bu
Ethan Cerami

Show All Co-Authors (53)

Author's Published Works

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Showing 1-19 of 19 results

Journals (16)

Conferences (3)

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Qianwen Wang(19)
Huamin Qu(10)
Nils Gehlenborg(8)
Yong Wang(5)
Chen Zhu-Tian(4)
Hong Kong University of Science and Technology, Hong Kong(4)
Harvard University, USA(3)
Harvard University, Cambridge, MA, USA(2)
Hong Kong University of Science and Technology, China(2)
Harvard Medical School, Boston, MA, USA(2)
IEEE Transactions on Visualization and Computer Graphics(16)
2023 IEEE Visualization and Visual Analytics (VIS)(2)
2023 IEEE VIS Workshop on Visualization Education, Literacy, and Activities (EduVis)(1)
IEEE(19)
Media(13)
Melbourne, Australia(3)
Visual Design(9)
Visualization Tool(9)
Visual Encoding(8)
Data Visualization(7)
Domain Experts(7)

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KNowNEt:Guided Health Information Seeking from LLMs via Knowledge Graph Integration

Youfu Yan;Yu Hou;Yongkang Xiao;Rui Zhang;Qianwen Wang

IEEE Transactions on Visualization and Computer Graphics

Year: 2025 | Volume: 31, Issue: 1 | Journal Article |

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The increasing reliance on Large Language Models (LLMs) for health information seeking can pose severe risks due to the potential for misinformation and the complexity of these topics. This paper introduces KnowNet a visualization system that integrates LLMs with Knowledge Graphs (KG) to provide enhanced accuracy and structured exploration. Specifically, for enhanced accuracy, KnowNet extracts tri...Show More

KNowNEt:Guided Health Information Seeking from LLMs via Knowledge Graph Integration

Youfu Yan;Yu Hou;Yongkang Xiao;Rui Zhang;Qianwen Wang

IEEE Transactions on Visualization and Computer Graphics

Year: 2025 | Volume: 31, Issue: 1 | Journal Article |

Enabling Multimodal User Interactions for Genomics Visualization Creation

Qianwen Wang;Xiao Liu;Man Qing Liang;Sehi L’Yi;Nils Gehlenborg

2023 IEEE Visualization and Visual Analytics (VIS)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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Visualization plays an important role in extracting insights from complex and large-scale genomics data. Traditional graphical user interfaces (GUIs) offer limited flexibility for custom visualizations. Our prior work, Gosling, enables expressive visualization creation using a grammar-based approach, but beginners may face challenges in constructing complex visualizations. To address this, we expl...Show More

Enabling Multimodal User Interactions for Genomics Visualization Creation

Qianwen Wang;Xiao Liu;Man Qing Liang;Sehi L’Yi;Nils Gehlenborg

2023 IEEE Visualization and Visual Analytics (VIS)

Year: 2023 | Conference Paper |

The Role of Visualization in Genomics Data Analysis Workflows: The Interviews

Sehi L’Yi;Qianwen Wang;Nils Gehlenborg

2023 IEEE Visualization and Visual Analytics (VIS)

Year: 2023 | Conference Paper |

Cited by: Papers (2)

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The diversity of genome-mapped data and analysis tasks makes it challenging for a single visualization tool to fulfill all visualization needs. To design a visualization tool that supports various genomics workflows of users, it is critical to first gain insights into the diverse workflows and the limitations of existing genomics tools for supporting them. In this paper, we conducted semi-structur...Show More

The Role of Visualization in Genomics Data Analysis Workflows: The Interviews

Sehi L’Yi;Qianwen Wang;Nils Gehlenborg

2023 IEEE Visualization and Visual Analytics (VIS)

Year: 2023 | Conference Paper |

Beyond Generating Code: Evaluating GPT on a Data Visualization Course

Zhutian Chen;Chenyang Zhang;Qianwen Wang;Jakob Troidl;Simon Warchol;Johanna Beyer;Nils Gehlenborg;Hanspeter Pfister

2023 IEEE VIS Workshop on Visualization Education, Literacy, and Activities (EduVis)

Year: 2023 | Conference Paper |

Cited by: Papers (8)

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This paper presents an empirical evaluation of the performance of the Generative Pre-trained Transformer (GPT) model in Harvard’s CS171 data visualization course. While previous studies have focused on GPT’s ability to generate code for visualizations, this study goes beyond code generation to evaluate GPT’s abilities in various visualization tasks, such as data interpretation, visualization desig...Show More

Beyond Generating Code: Evaluating GPT on a Data Visualization Course

Zhutian Chen;Chenyang Zhang;Qianwen Wang;Jakob Troidl;Simon Warchol;Johanna Beyer;Nils Gehlenborg;Hanspeter Pfister

2023 IEEE VIS Workshop on Visualization Education, Literacy, and Activities (EduVis)

Year: 2023 | Conference Paper |

Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing

Qianwen Wang;Kexin Huang;Payal Chandak;Marinka Zitnik;Nils Gehlenborg

IEEE Transactions on Visualization and Computer Graphics

Year: 2023 | Volume: 29, Issue: 1 | Journal Article |

Cited by: Papers (31)

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Whether AI explanations can help users achieve specific tasks efficiently (i.e., usable explanations) is significantly influenced by their visual presentation. While many techniques exist to generate explanations, it remains unclear how to select and visually present AI explanations based on the characteristics of domain users. This paper aims to understand this question through a multidisciplinar...Show More

Extending the Nested Model for User-Centric XAI: A Design Study on GNN-based Drug Repurposing

Qianwen Wang;Kexin Huang;Payal Chandak;Marinka Zitnik;Nils Gehlenborg

IEEE Transactions on Visualization and Computer Graphics

Year: 2023 | Volume: 29, Issue: 1 | Journal Article |

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

Year: 2023 | Volume: 29, Issue: 1 | Journal Article |

Cited by: Papers (10)

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Interpretation of genomics data is critically reliant on the application of a wide range of visualization tools. A large number of visualization techniques for genomics data and different analysis tasks pose a significant challenge for analysts: which visualization technique is most likely to help them generate insights into their data? Since genomics analysts typically have limited training in da...Show More

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

Year: 2023 | Volume: 29, Issue: 1 | Journal Article |

Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis

Furui Cheng;Mark S Keller;Huamin Qu;Nils Gehlenborg;Qianwen Wang

IEEE Transactions on Visualization and Computer Graphics

Year: 2023 | Volume: 29, Issue: 1 | Journal Article |

Cited by: Papers (5)

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Reference-based cell-type annotation can significantly reduce time and effort in single-cell analysis by transferring labels from a previously-annotated dataset to a new dataset. However, label transfer by end-to-end computational methods is challenging due to the entanglement of technical (e.g., from different sequencing batches or techniques) and biological (e.g., from different cellular microen...Show More

Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis

Furui Cheng;Mark S Keller;Huamin Qu;Nils Gehlenborg;Qianwen Wang

IEEE Transactions on Visualization and Computer Graphics

Year: 2023 | Volume: 29, Issue: 1 | Journal Article |

GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks

Zhihua Jin;Yong Wang;Qianwen Wang;Yao Ming;Tengfei Ma;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2023 | Volume: 29, Issue: 6 | Journal Article |

Cited by: Papers (21)

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Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and us...Show More

GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks

Zhihua Jin;Yong Wang;Qianwen Wang;Yao Ming;Tengfei Ma;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2023 | Volume: 29, Issue: 6 | Journal Article |

Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization

Sehi LYi;Qianwen Wang;Fritz Lekschas;Nils Gehlenborg

IEEE Transactions on Visualization and Computer Graphics

Year: 2022 | Volume: 28, Issue: 1 | Journal Article |

Cited by: Papers (35)

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The combination of diverse data types and analysis tasks in genomics has resulted in the development of a wide range of visualization techniques and tools. However, most existing tools are tailored to a specific problem or data type and offer limited customization, making it challenging to optimize visualizations for new analysis tasks or datasets. To address this challenge, we designed Gosling-a ...Show More

Gosling: A Grammar-based Toolkit for Scalable and Interactive Genomics Data Visualization

Sehi LYi;Qianwen Wang;Fritz Lekschas;Nils Gehlenborg

IEEE Transactions on Visualization and Computer Graphics

Year: 2022 | Volume: 28, Issue: 1 | Journal Article |

ThreadStates: State-based Visual Analysis of Disease Progression

Qianwen Wang;Tali Mazor;Theresa A Harbig;Ethan Cerami;Nils Gehlenborg

IEEE Transactions on Visualization and Computer Graphics

Year: 2022 | Volume: 28, Issue: 1 | Journal Article |

Cited by: Papers (12)

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A growing number of longitudinal cohort studies are generating data with extensive patient observations across multiple timepoints. Such data offers promising opportunities to better understand the progression of diseases. However, these observations are usually treated as general events in existing visual analysis tools. As a result, their capabilities in modeling disease progression are not full...Show More

ThreadStates: State-based Visual Analysis of Disease Progression

Qianwen Wang;Tali Mazor;Theresa A Harbig;Ethan Cerami;Nils Gehlenborg

IEEE Transactions on Visualization and Computer Graphics

Year: 2022 | Volume: 28, Issue: 1 | Journal Article |

A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization

Qianwen Wang;Chen Zhu-Tian;Yong Wang;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2022 | Volume: 28, Issue: 12 | Journal Article |

Cited by: Papers (50)

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Inspired by the great success of machine learning (ML), researchers have applied ML techniques to visualizations to achieve a better design, development, and evaluation of visualizations. This branch of studies, known as ML4VIS, is gaining increasing research attention in recent years. To successfully adapt ML techniques for visualizations, a structured understanding of the integration of ML4VIS i...Show More

A Survey on ML4VIS: Applying Machine Learning Advances to Data Visualization

Qianwen Wang;Chen Zhu-Tian;Yong Wang;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2022 | Volume: 28, Issue: 12 | Journal Article |

Visual Analysis of Discrimination in Machine Learning

Qianwen Wang;Zhenhua Xu;Chen Zhu-Tian;Yong Wang;Shixia Liu;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2021 | Volume: 27, Issue: 2 | Journal Article |

Cited by: Papers (25)

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The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization t...Show More

Visual Analysis of Discrimination in Machine Learning

Qianwen Wang;Zhenhua Xu;Chen Zhu-Tian;Yong Wang;Shixia Liu;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2021 | Volume: 27, Issue: 2 | Journal Article |

SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects

Chuan Bu;Quanjie Zhang;Qianwen Wang;Jian Zhang;Michael Sedlmair;Oliver Deussen;Yunhai Wang

IEEE Transactions on Visualization and Computer Graphics

Year: 2021 | Volume: 27, Issue: 2 | Journal Article |

Cited by: Papers (10)

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In this paper, we propose SineStream, a new variant of streamgraphs that improves their readability by minimizing sine illusion effects. Such effects reflect the tendency of humans to take the orthogonal rather than the vertical distance between two curves as their distance. In SineStream, we connect the readability of streamgraphs with minimizing sine illusions and by doing so provide a perceptua...Show More

SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects

Chuan Bu;Quanjie Zhang;Qianwen Wang;Jian Zhang;Michael Sedlmair;Oliver Deussen;Yunhai Wang

IEEE Transactions on Visualization and Computer Graphics

Year: 2021 | Volume: 27, Issue: 2 | Journal Article |

HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models

Qianwen Wang;William Alexander;Jack Pegg;Huamin Qu;Min Chen

IEEE Transactions on Visualization and Computer Graphics

Year: 2021 | Volume: 27, Issue: 2 | Journal Article |

Cited by: Papers (6)

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In this paper, we present a visual analytics tool for enabling hypothesis-based evaluation of machine learning (ML) models. We describe a novel ML-testing framework that combines the traditional statistical hypothesis testing (commonly used in empirical research) with logical reasoning about the conclusions of multiple hypotheses. The framework defines a controlled configuration for testing a numb...Show More

HypoML: Visual Analysis for Hypothesis-based Evaluation of Machine Learning Models

Qianwen Wang;William Alexander;Jack Pegg;Huamin Qu;Min Chen

IEEE Transactions on Visualization and Computer Graphics

Year: 2021 | Volume: 27, Issue: 2 | Journal Article |

Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline

Chen Zhu-Tian;Yun Wang;Qianwen Wang;Yong Wang;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2020 | Volume: 26, Issue: 1 | Journal Article |

Cited by: Papers (37)

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Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for automated infographics design. As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-...Show More

Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline

Chen Zhu-Tian;Yun Wang;Qianwen Wang;Yong Wang;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2020 | Volume: 26, Issue: 1 | Journal Article |

DeepDrawing: A Deep Learning Approach to Graph Drawing

Yong Wang;Zhihua Jin;Qianwen Wang;Weiwei Cui;Tengfei Ma;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2020 | Volume: 26, Issue: 1 | Journal Article |

Cited by: Papers (27)

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Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algorithm-specific parameters iteratively by comparing the corresponding drawing results in order to achieve a desired visual effect. This trial and error process is often tedious and time-consuming, especially for non-expert...Show More

DeepDrawing: A Deep Learning Approach to Graph Drawing

Yong Wang;Zhihua Jin;Qianwen Wang;Weiwei Cui;Tengfei Ma;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2020 | Volume: 26, Issue: 1 | Journal Article |

Visual Genealogy of Deep Neural Networks

Qianwen Wang;Jun Yuan;Shuxin Chen;Hang Su;Huamin Qu;Shixia Liu

IEEE Transactions on Visualization and Computer Graphics

Year: 2020 | Volume: 26, Issue: 11 | Journal Article |

Cited by: Papers (19)

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A comprehensive and comprehensible summary of existing deep neural networks (DNNs) helps practitioners understand the behaviour and evolution of DNNs, offers insights for architecture optimization, and sheds light on the working mechanisms of DNNs. However, this summary is hard to obtain because of the complexity and diversity of DNN architectures. To address this issue, we develop DNN Genealogy, ...Show More

Visual Genealogy of Deep Neural Networks

Qianwen Wang;Jun Yuan;Shuxin Chen;Hang Su;Huamin Qu;Shixia Liu

IEEE Transactions on Visualization and Computer Graphics

Year: 2020 | Volume: 26, Issue: 11 | Journal Article |

MARVisT: Authoring Glyph-Based Visualization in Mobile Augmented Reality

Chen Zhu-Tian;Yijia Su;Yifang Wang;Qianwen Wang;Huamin Qu;Yingcai Wu

IEEE Transactions on Visualization and Computer Graphics

Year: 2020 | Volume: 26, Issue: 8 | Journal Article |

Cited by: Papers (35)

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Recent advances in mobile augmented reality (AR) techniques have shed new light on personal visualization for their advantages of fitting visualization within personal routines, situating visualization in a real-world context, and arousing users’ interests. However, enabling non-experts to create data visualization in mobile AR environments is challenging given the lack of tools that allow in-situ...Show More

MARVisT: Authoring Glyph-Based Visualization in Mobile Augmented Reality

Chen Zhu-Tian;Yijia Su;Yifang Wang;Qianwen Wang;Huamin Qu;Yingcai Wu

IEEE Transactions on Visualization and Computer Graphics

Year: 2020 | Volume: 26, Issue: 8 | Journal Article |

Narvis: Authoring Narrative Slideshows for Introducing Data Visualization Designs

Qianwen Wang;Zhen Li;Siwei Fu;Weiwei Cui;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2019 | Volume: 25, Issue: 1 | Journal Article |

Cited by: Papers (22)

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Visual designs can be complex in modern data visualization systems, which poses special challenges for explaining them to the non-experts. However, few if any presentation tools are tailored for this purpose. In this study, we present Narvis, a slideshow authoring tool designed for introducing data visualizations to non-experts. Narvis targets two types of end users: teachers, experts in data visu...Show More

Narvis: Authoring Narrative Slideshows for Introducing Data Visualization Designs

Qianwen Wang;Zhen Li;Siwei Fu;Weiwei Cui;Huamin Qu

IEEE Transactions on Visualization and Computer Graphics

Year: 2019 | Volume: 25, Issue: 1 | Journal Article |

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