World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
3510
World Ranking
13748
National Ranking
5461

Overview

Chaoli Wang is affiliated with the University of Notre Dame in the United States. Their research primarily centers on computer science, with a strong focus on computer vision and pattern recognition, artificial intelligence, and computer graphics and computer-aided design. Additional subfields include statistical and nonlinear physics as well as radiology, nuclear medicine, and imaging.

The scientist's work spans a variety of topics, notably data visualization and analytics, advanced vision and imaging, computer graphics and visualization techniques, advanced image processing techniques, image and signal denoising methods, advanced neural network applications, and generative adversarial networks and image synthesis.

Chaoli Wang has published extensively in several venues. Frequent publication outlets include:

  • IEEE Transactions on Visualization and Computer Graphics
  • arXiv (Cornell University)
  • Computers & Graphics
  • Visual Informatics
  • IEEE Computer Graphics and Applications

Recent papers by or involving Chaoli Wang include:

  • DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization, 2022, IEEE Transactions on Visualization and Computer Graphics
  • An Annotation Sparsification Strategy for 3D Medical Image Segmentation via Representative Selection and Self-Training, 2020, Proceedings of the AAAI Conference on Artificial Intelligence

Other notable recent works, authored by frequent collaborators, reflect the scientist's involvement in time-varying data analysis and visualization:

  • SSR-TVD: Spatial Super-Resolution for Time-Varying Data Analysis and Visualization, 2020, IEEE Transactions on Visualization and Computer Graphics
  • CoordNet: Data Generation and Visualization Generation for Time-Varying Volumes via a Coordinate-Based Neural Network, 2022, IEEE Transactions on Visualization and Computer Graphics
  • V2V: A Deep Learning Approach to Variable-to-Variable Selection and Translation for Multivariate Time-Varying Data, 2020, IEEE Transactions on Visualization and Computer Graphics

Frequent co-authors in their network include Jun Han, Danny Z. Chen, Siyuan Yao, Siavash Ghorbany, and Ming Hu, indicating a collaborative research environment and cross-disciplinary engagement.

Best Publications

  • In Situ Visualization for Large-Scale Combustion Simulations

    Hongfeng Yu;Chaoli Wang;Ray W Grout;Jacqueline H Chen

  • Importance-Driven Time-Varying Data Visualization

    Chaoli Wang;Hongfeng Yu;Kwan-Liu Ma

  • Information Theory in Scientific Visualization

    Chaoli Wang;Han-Wei Shen

  • Massively parallel volume rendering using 2-3 swap image compositing

    Hongfeng Yu;Chaoli Wang;Kwan-Liu Ma

  • High dimensional direct rendering of time-varying volumetric data

    J. Woodring;Chaoli Wang;Han-Wei Shen

  • In-situ processing and visualization for ultrascale simulations

    Kwan-Liu Ma;Chaoli Wang;Hongfeng Yu;Anna Tikhonova

  • Parallel hierarchical visualization of large time-varying 3D vector fields

    Hongfeng Yu;Chaoli Wang;Kwan-Liu Ma

  • Hierarchical Streamline Bundles

    Hongfeng Yu;Chaoli Wang;Ching-Kuang Shene;J. H. Chen

  • A Unified Approach to Streamline Selection and Viewpoint Selection for 3D Flow Visualization

    Jun Tao;Jun Ma;Chaoli Wang;Ching-Kuang Shene

  • FlowNet: A Deep Learning Framework for Clustering and Selection of Streamlines and Stream Surfaces

    Jun Han;Jun Tao;Chaoli Wang

  • A New Ensemble Learning Framework for 3D Biomedical Image Segmentation.

    Hao Zheng;Yizhe Zhang;Lin Yang;Peixian Liang

  • TransGraph: Hierarchical Exploration of Transition Relationships in Time-Varying Volumetric Data

    Yi Gu;Chaoli Wang

  • Feature-Preserving Volume Data Reduction and Focus+Context Visualization

    Yu-Shuen Wang;Chaoli Wang;Tong-Yee Lee;Kwan-Liu Ma

  • A multiresolution volume rendering framework for large-scale time-varying data visualization

    Chaoli Wang;Jinzhu Gao;Liya Li;Han-Wei Shen

  • Massively parallel volume rendering using 2-3 swap image compositing

    Hongfeng Yu;Chaoli Wang;Kwan Liu Ma

  • Correlation study of time-varying multivariate climate data sets

    Jeffrey Sukharev;Chaoli Wang;Kwan-Liu Ma;Andrew T. Wittenberg

  • Application-Driven Compression for Visualizing Large-Scale Time-Varying Data

    Chaoli Wang;Hongfeng Yu;Kwan-Liu Ma

  • TSR-TVD: Temporal Super-Resolution for Time-Varying Data Analysis and Visualization

    Jun Han;Chaoli Wang

  • Interactive Level-of-Detail Selection Using Image-Based Quality Metric for Large Volume Visualization

    C. Wang;A. Garcia;H.-W. Shen

  • LOD Map - A Visual Interface for Navigating Multiresolution Volume Visualization

    C. Wang;H.-W. Shen

  • Parallel multiresolution volume rendering of large data sets with error-guided load balancing

    Chaoli Wang;Jinzhu Gao;Han-Wei Shen

  • Biomedical Image Segmentation via Representative Annotation

    Hao Zheng;Lin Yang;Jianxu Chen;Jun Han

  • SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization

    Li Guo;Shaojie Ye;Jun Han;Hao Zheng

  • A sketch-based interface for classifying and visualizing vector fields

    Jishang Wei;Chaoli Wang;Hongfeng Yu;Kwan-Liu Ma

Frequent Co-Authors

Kwan-Liu Ma
Kwan-Liu Ma University of California, Davis
Danny Z. Chen
Danny Z. Chen University of Notre Dame
Han-Wei Shen
Han-Wei Shen The Ohio State University
Nitesh V. Chawla
Nitesh V. Chawla University of Notre Dame
Jacqueline H. Chen
Jacqueline H. Chen Sandia National Laboratories
Hanghang Tong
Hanghang Tong University of Illinois at Urbana-Champaign
Tong-Yee Lee
Tong-Yee Lee National Cheng Kung University
Andrew T. Wittenberg
Andrew T. Wittenberg Geophysical Fluid Dynamics Laboratory
Sidney K. D'Mello
Sidney K. D'Mello University of Colorado Boulder
Gordon G. Parker
Gordon G. Parker Michigan Technological University

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