Hanqi Guo

Feature extraction and tracking

Feature tracking is a core topic in scientific visualization for understanding dynamic behaviors in time-varying simulation and experimental data. By tracking features such as extrema, vortex cores, and boundary surfaces, one can highlight key regions in visualization, reduce data to store, and enable further analysis based on the dynamics of features in scientific data.

We have developed a general framework, namely FTK, that delivers a collection of feature-tracking tools to end users, scales feature-tracking algorithms in distributed and parallel environments, and simplifies the development of new feature-tracking algorithms. We applied FTK to various applications including fusion energy sciences, superconductivity, and high-speed imaging experiments.

  • Hanqi Guo, David Lenz, Jiayi Xu, Xin Liang, Wenbin He, Iulian R. Grindeanu, Han-Wei Shen, Tom Peterka, Todd Munson, and Ian Foster
    FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking
    IEEE Transactions on Visualization and Computer Graphics, 2021. (In Preprint)
    | DOI | arXiv |

  • Jiayi Xu, Hanqi Guo, Han-Wei Shen, Mukund Raj, Xueqiao Xu, Xueyun Wang, Zhehui Wang, and Tom Peterka
    Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis
    IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE PacificVis 2021), 2021. (Accepted)
    | DOI | arXiv |

  • Zhehui Wang, Jiayi Xu, Yao E. Kovach, Bradley T. Wolfe, Edward Thomas Jr., Hanqi Guo, John E. Foster, and Han-Wei Shen
    Microparticle cloud imaging and tracking for data-driven plasma science
    Physics of Plasmas, AIP Publishing, 27(3):033703, 2020.
    | DOI | arXiv |

  • Martin Imre, Jun Han, Julien Dominski, Michael Churchill, Ralph Kube, Choong-Seock Chang, Tom Peterka, Hanqi Guo, and Chaoli Wang
    ContourNet: Salient Local Contour Identification for Blob Detection in Plasma Fusion Simulation Data
    In ISVC'19: Proceedings of International Symposium on Visual Computing, pages 289-301, Lake Tahoe, NV, 2019.
    | DOI | PDF | GitHub | Press Release |

  • Hanqi Guo, Carolyn L. Phillps, Tom Peterka, Dmitry Karpeyev, and Andreas Glatz
    Extracting, Tracking, and Visualizing Magnetic Flux Vortices in 3D Complex-Valued Superconductor Simulation Data
    IEEE Transactions on Visualization and Computer Graphics (VIS '15), 22(1):827-836, 2016.
    | DOI | PDF (3.8 MB) | Video (11 MB) | Video Preview | Github |

  • Carolyn L. Phillips, Hanqi Guo, Tom Peterka, Dmitry Karpeyev, and Andreas Glatz
    Tracking Vortices in Superconductors: Extracting Singularities from a Discretized Complex Scalar Field Evolving in Time
    Physical Reivew E, 93(023305), 2016.
    | DOI | PDF |

AI, machine learning, and deep learning

As scientists anticipate the benefits of exascale computing, the lack of novel solutions to process data at scale and calibrate the simulation parameters has become a significant roadblock to further accelerating scientific discovery. The goal of our NSF project on deep learning for visualization is to develop a new end-to-end data analysis and feature extraction workflow based on deep neural networks to help computational scientists address three major challenges: (1) identify important simulation parameters and generate the essential data for analysis, (2) transform the simulation data to compact feature representations to convey the most insight, and (3) design scalable visualization algorithms coupled with large-scale simulations to glean insight into their scientific problems. Working with domain scientists in jet engine design, climate models, cardio/cerebrovascular flow, superconductivity, and fusion energy, the team will demonstrate how deep learning techniques can help extract features from vast amounts of simulation data and navigate in the huge simulation parameter space. Through summer internships and project collaborations, this research will create opportunities for graduate and undergraduate students, including students from underrepresented groups, to participate in key research initiatives with leading scientists. Through the planned annual summer school on "Deep Learning for Visualization," the research results will enable visualization researchers and a broader community to incorporate the principles and practice of deep learning techniques developed.

  • Wenbin He, Junpeng Wang, Hanqi Guo, Ko-Chih Wang, Han-Wei Shen, Mukund Raj, Youssef S. G. Nashed, and Tom Peterka
    InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations
    IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS 2019), 26(1):23-33, 2020. (Best Paper Award)
    | DOI | arXiv | PDF (7.5 MB) | Press Release |

  • Jun Han, Jun Tao, Hao Zheng, Hanqi Guo, Danny Z. Chen, and Chaoli Wang
    Flow Field Reduction via Reconstructing Vector Data from 3D Streamlines Using Deep Learning
    IEEE Computer Graphics and Applications, Special Issue on Visual Computing with Deep Learning, 39(4):54-67, 2019.
    | DOI | PDF |

  • Wenbin He, Junpeng Wang, Hanqi Guo, Han-Wei Shen, and Tom Peterka
    CECAV: Collective Ensemble Comparison and Visualization using Deep Neural Networks
    Journal of Visual Informatics, 4(2):109--121, 2020.
    | DOI | PDF |

Parallel and in situ visualization

Scientific simulations on modern HPC systems produce data far faster than it can be written to disk, demanding visualization and analysis algorithms that scale across thousands of compute nodes and run in situ alongside the running simulation. Our research in this area develops parallel and distributed algorithms for particle tracing, feature tracking, union-find, and large-scale data reduction, together with in situ workflows that couple visualization directly with running fusion, superconductivity, and combustion simulations on DOE supercomputers.

  • Hanqi Guo, David Lenz, Jiayi Xu, Xin Liang, Wenbin He, Iulian R. Grindeanu, Han-Wei Shen, Tom Peterka, Todd Munson, and Ian Foster
    FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking
    IEEE Transactions on Visualization and Computer Graphics, 2021. (In Preprint)
    | DOI | arXiv |

  • Jiayi Xu, Hanqi Guo, Han-Wei Shen, Mukund Raj, Xueqiao Xu, Xueyun Wang, Zhehui Wang, and Tom Peterka
    Asynchronous and Load-Balanced Union-Find for Distributed and Parallel Scientific Data Visualization and Analysis
    IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE PacificVis 2021), 2021. (Accepted)
    | DOI | arXiv |

  • Hanqi Guo, Wenbin He, Sangmin Seo, Han-Wei Shen, Emil Mihai Constantinescu, Chunhui Liu, and Tom Peterka
    Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis
    IEEE Transactions on Visualization and Computer Graphics, 25(9):2710-2724, 2019.
    | DOI | PDF (1.8 MB) |

  • Jiang Zhang, Hanqi Guo, Fan Hong, Xiaoru Yuan, and Tom Peterka
    Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing
    IEEE Transactions on Visualization and Computer Graphics (VIS '17), 24(1):954-963, 2018.
    | DOI | PDF (3.5 MB) | Video Preview |

  • Wenbin He, Hanqi Guo, Tom Peterka, Sheng Di, Franck Cappello, and Han-Wei Shen
    Parallel Partial Reduction for Large-Scale Data Analysis and Visualization
    In LDAV'18: Proceedings of IEEE Symposium on Large Data Analysis and Visualization, pages 45-55, 2018. (Honorable Mention Award)
    | DOI | PDF | Presentation |

  • Hanqi Guo, Xiaoru Yuan, Jian Huang, and Xiaomin Zhu
    Coupled Ensemble Flow Line Advection and Analysis
    IEEE Transactions on Visualization and Computer Graphics (VIS '13), 19(12):2733-2742, 2013.
    | DOI | PDF (633 KB) | Video (8.2 MB) |

  • Jiang Zhang, Hanqi Guo, Xiaoru Yuan, and Tom Peterka
    Dynamic Data Repartitioning for Load-Balanced Parallel Particle Tracing
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis '18), pages 86-95, Kobe, Japan, April 10-13, 2018.
    | DOI | PDF (4.0 MB) |

  • Jiang Zhang, Hanqi Guo, and Xiaoru Yuan
    Efficient Unsteady Flow Visualization with High-Order Access Dependencies
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis '16), Taipei, Apr. 12-15, pages 82-97, 2016.
    | DOI | PDF (2.4 MB) |

  • Hanqi Guo, Jiang Zhang, Richen Liu, Lu Liu, Xiaoru Yuan, Jian Huang, Xiangfei Meng, and Jingshan Pan
    Advection-based Sparse Data Management for Visualizing Unsteady Flow
    IEEE Transactions on Visualization and Computer Graphics (VIS '14), 20(12):2555-2564, 2014.
    | DOI | PDF (4.8 MB) |

  • Wenbin He, Junpeng Wang, Hanqi Guo, Ko-Chih Wang, Han-Wei Shen, Mukund Raj, Youssef S. G. Nashed, and Tom Peterka
    InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations
    IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS 2019), 26(1):23-33, 2020. (Best Paper Award)
    | DOI | arXiv | PDF (7.5 MB) | Press Release |

  • Hanqi Guo, Tom Peterka, and Andreas Glatz
    In Situ Magnetic Flux Vortex Visualization in Time-Dependent Ginzburg-Landau Superconductor Simulations
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis '17), Seuol, Korea, April 18-21, pages 71-80, 2017.
    | DOI | PDF (11 MB) | Video (25 MB) | Github |

  • Jong Youl Choi, Choong-Seock Chang, Julien Dominski, Scott Klasky, Gabriele Merlo, Eric Suchyta, Mark Ainsworth, Bryce Allen, Franck Cappello, Michael Churchill, Philip Davis, Sheng Di, Greg Eisenhauer, Stephane Ethier, Ian Foster, Berk Geveci, Hanqi Guo, Kevin Huck, Frank Jenko, Mark Kim, James Kress, Seung-Hoe Ku, Qing Liu, Jeremy Logan, Allen Malony, Kshitij Mehta, Kenneth Moreland, Todd Munson, Manish Parashar, Tom Peterka, Norbert Podhorszki, Dave Pugmire, Ozan Tugluk, Ruonan Wang, Ben Whitney, Matthew Wolf, and Chad Wood
    Coupling Exascale Multiphysics Applications: Methods and Lessons Learned
    In Proceedings of IEEE International Conference on eScience 2018, Amsterdam, Netherlands, October 29-November 1, pages 442-452, 2018.
    | DOI | PDF |

  • Mark Kim, James Kress, Jong Youl Choi, Norbert Odhorszki, Scott Klasky, Matthew Wolf, Kshitij Mehta, Kevin Huck, Berk Geveci, Sujin Philip, Robert Maynard, Hanqi Guo, Thomas Peterka, Kenneth Moreland, Choong-Seock Chang, Julien Dominski, Michael Churchill, and David Pugmire
    In Situ Analysis and Visualization of Fusion Simulations: Lessons Learned
    In Proceedings of ISC Workshop on In Situ Visualization (WOIV '18), Frankfurt, Germany, June 28, 2018.
    | DOI | PDF |

  • Ian Foster, Mark Ainsworth, Bryce Allen, Julie Bessac, Franck Cappello, Jong Youl Choi, Emil Constantinescu, Philip E. Davis, Sheng Di, Wendy Di, Hanqi Guo, Scott Klasky, Kerstin Kleese Van Dam, Tahsin Kurc, Qing Liu, Abid Malik, Kshitij Mehta, Klaus Mueller, Todd Munson, George Ostouchov, Manish Parashar, Tom Peterka, Line Pouchard, Dingwen Tao, Ozan Tugluk, Stefan Wild, Matthew Wolf, Justin M. Wozniak, Wei Xu, and Shinjae Yoo
    Computing Just What You Need: Online Data Analysis and Reduction at Extreme Scales
    In Proceedings of International Conference on Parallel and Distributed Computing (EuroPar), pages 3-19, 2017.
    | DOI | PDF |

Feature-preserving compression

Exascale simulations and high-resolution experiments routinely generate data at rates that far exceed available I/O and storage bandwidth, forcing scientists to rely on lossy compression. Generic error-bounded compressors guarantee pointwise error, but they can silently destroy the topological and structural features (critical points, Morse–Smale complexes, contour trees, integral curves, vector field trajectories, spectra, and other quantities of interest) that downstream analyses actually depend on. Our group develops a family of feature-preserving lossy compressors—including the TopoSZ, MSz, TspSZ, TFZ, FFCz, EXaCTz, and related tools—that augment state-of-the-art SZ/ZFP-style compressors with provable guarantees on the preservation of these features, while remaining competitive in compression ratio and throughput on CPU, GPU, and distributed-memory systems. This line of work is conducted with collaborators at Argonne, Utah, Missouri S&T, and others, and is applied to fusion, climate, cosmology, fluid dynamics, and materials science datasets.

  • Yuxiao Li, Xin Liang, Bei Wang, and Hanqi Guo
    Preserving Discrete Morse–Smale Complexes in Error-Bounded Lossy Compression
    IEEE Transactions on Visualization and Computer Graphics, 2026. (In Press)
    | DOI | arXiv |

  • Congrong Ren, Robert Underwood, Sheng Di, Emrecan Kutay, Zarija Lukic, Aylin Yener, Franck Cappello, and Hanqi Guo
    FFCz: Fast Fourier Correction for Spectrum-Preserving Lossy Compression of Scientific Data
    In IPDPS '26: Proceedings of the 40th IEEE International Parallel and Distributed Processing Symposium, 2026. (Accepted)
    | DOI | arXiv |

  • Yuxiao Li, Mingze Xia, Xin Liang, Bei Wang, Robert Underwood, Sheng Di, Hemant Sharma, Dishant Beniwal, Franck Cappello, and Hanqi Guo
    pMSz: A Distributed Parallel Algorithm for Correcting Morse-Smale Segmentations for Lossy Compression
    In IPDPS '26: Proceedings of the 40th IEEE International Parallel and Distributed Processing Symposium, 2026. (Accepted)
    | DOI | arXiv |

  • Mingze Xia, Yuxiao Li, Pu Jiao, Bei Wang, Xin Liang, and Hanqi Guo
    Time-varying Vector Field Compression with Preserved Critical Point Trajectories
    In ICDE '26: Proceedings of the 42nd IEEE International Conference on Data Engineering, 2026. (Accepted)
    | DOI | arXiv |

  • Yuxiao Li, Mingze Xia, Xin Liang, Bei Wang, and Hanqi Guo
    EXaCTz: Guaranteed Extremum Graph and Contour Tree Preservation for Distributed- and GPU-Parallel Lossy Compression
    arXiv:2604.01397 [cs.DC], 2026.
    | arXiv |

  • Tripti Agarwal, Sheng Di, Xin Liang, Zhaoyuan Su, Yuxiao Li, Ganesh Gopalakrishnan, Hanqi Guo, and Franck Cappello
    TopoSZp: Lightweight Topology-Aware Error-controlled Compression for Scientific Data
    arXiv:2602.17552 [cs.DC], 2026.
    | arXiv |

  • Congrong Ren, Sheng Di, Katrin Heitmann, Franck Cappello, and Hanqi Guo
    Preserving Clusters in Error-Bounded Lossy Compression of Particle Data
    arXiv:2604.18801 [cs.LG], 2026.
    | arXiv |

  • Nathaniel Gorski, Xin Liang, Hanqi Guo, and Bei Wang
    TFZ: Topology-Preserving Compression of 2D Symmetric and Asymmetric Second-Order Tensor Fields
    IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS 2025), 2025. (In Preprint)
    | DOI | arXiv |

  • Mingze Xia, Bei Wang, Yuxiao Li, Pu Jiao, Xin Liang, and Hanqi Guo
    TspSZ: An Efficient Parallel Error-Bounded Lossy Compressor for Topological Skeleton Preservation
    In ICDE '25: Proceedings of the 41st IEEE International Conference on Data Engineering, pp. 3682-3695, 2025.
    | DOI |

  • Nathaniel Gorski, Xin Liang, Hanqi Guo, Lin Yan, and Bei Wang
    A General Framework for Augmenting Lossy Compressors with Topological Guarantees
    IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE PacificVis 2025 Journal Track), 31(6):3693-3705, 2025.
    | DOI | arXiv |

  • Yuxiao Li, Xin Liang, Bei Wang, Yongfeng Qiu, Lin Yan, and Hanqi Guo
    MSz: An Efficient Parallel Algorithm for Correcting Morse-Smale Segmentations in Error-Bounded Lossy Compressors
    IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS 2024), 31(1):130-140, 2025.
    | DOI | arXiv | GitHub |

  • Mingze Xia, Sheng Di, Franck Cappello, Pu Jiao, Kai Zhao, Jinyang Liu, Xuan Wu, Xin Liang, and Hanqi Guo
    Preserving Topological Feature with Sign-of-Determinant Predicates in Lossy Compression: A Case Study of Vector Field Critical Points
    In ICDE '24: Proceedings of IEEE International Conference on Data Engineering, Utrecht, Netherlands, pp. 4979-4992, 2024.
    | DOI |

  • Lin Yan, Xin Liang, Hanqi Guo, and Bei Wang
    TopoSZ: Preserving Topology in Error-Bounded Lossy Compression
    IEEE Transactions on Visualization and Computer Graphics (Proc. IEEE VIS 2023), 30(1):1302-1312, 2024.
    | DOI | arXiv |

  • Xin Liang, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka, and Hanqi Guo
    Toward Feature-Preserving Vector Field Compression
    IEEE Transactions on Visualization and Computer Graphics, 29(12):5434-5450, 2023.
    | DOI | PDF (7.7 MB) |

  • Pu Jiao, Sheng Di, Hanqi Guo, Kai Zhao, Jiannan Tian, Dingwen Tao, Xin Liang, and Franck Cappello
    Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data
    In Proceedings of the VLDB Endowment, 16(4):697-710, 2022.
    | DOI |

  • Xin Liang, Hanqi Guo, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, and Tom Peterka
    Toward Feature Preserving 2D and 3D Vector Field Compression
    In Proceedings of IEEE Pacific Visualization Symposium, Tianjin, China, June 3-5, pages 81-90, 2020.
    | DOI | PDF (1.4 MB) |

Data reduction and compression

Beyond the feature-preserving line of work above, our group contributes to the broader scientific data-reduction toolchain: error-bounded lossy compressors that achieve high compression ratios on regular and unstructured grids, frameworks for assessing compression quality, and reuse-oriented schemes that share derived data products such as integral curves across multiple visualization tasks. These efforts are conducted in close collaboration with the SZ ecosystem at Argonne and partner institutions.

  • Xin Liang, Hanqi Guo, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, and Tom Peterka
    Toward Feature Preserving 2D and 3D Vector Field Compression
    In Proceedings of IEEE Pacific Visualization Symposium, Tianjin, China, June 3-5, pages 81-90, 2020.
    | DOI | PDF (1.4 MB) |

  • Dingwen Tao, Sheng Di, Hanqi Guo, Zizhong Chen, and Franck Cappello
    Z-checker: A Framework for Assessing Lossy Compression of Scientific Data
    International Journal of High Performance Computing Applications, 33(2):285-303, 2019.
    | DOI | arXiv | PDF |

  • Xin Liang, Sheng Di, Dingwen Tao, Sihuan Li, Shaomeng Li, Hanqi Guo, Zizhong Chen, and Franck Cappello
    Error-Controlled Lossy Compression Optimized for High Compression Ratios of Scientific Datasets
    In Proceedings of IEEE International Conference on BIG DATA, pages 438-447, Seattle, WA, December 10-13, 2018.
    | DOI | PDF |

  • Fan Hong, Chongke Bi, Hanqi Guo, Kenji Ono, and Xiaoru Yuan
    Compression-based Integral Curve Data Reuse Framework for Flow Visualization
    Journal of Visualization, 20(4):859-874, 2017.
    | DOI | PDF |

Uncertainty and ensemble visualization

Scientific simulations and observational data are inherently uncertain—driven by stochastic forcings, ensemble parameter sweeps, or measurement noise—and faithfully visualizing this uncertainty is essential for trustworthy scientific decision-making. We develop uncertainty-aware feature-extraction and tracking algorithms (Lagrangian coherent structures, particle traces, surface density estimates) and comparative visualization techniques for large ensembles of simulation data, with applications in weather forecasting, atmospheric science, and unsteady flow analysis.

  • Hanqi Guo, Wenbin He, Sangmin Seo, Han-Wei Shen, Emil Mihai Constantinescu, Chunhui Liu, and Tom Peterka
    Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis
    IEEE Transactions on Visualization and Computer Graphics, 25(9):2710-2724, 2019.
    | DOI | PDF (1.8 MB) |

  • Hanqi Guo, Wenbin He, Tom Peterka, Han-Wei Shen, Scott M. Collis, and Jonathan J. Helmus
    Finite-Time Lyapunov Exponents and Lagrangian Coherent Structures in Uncertain Unsteady Flows
    IEEE Transactions on Visualization and Computer Graphics (PacificVis '16), 22(6):1672-1682, 2016.
    | DOI | PDF (2.8 MB) | Science Highlights |

  • Hanqi Guo, Xiaoru Yuan, Jian Huang, and Xiaomin Zhu
    Coupled Ensemble Flow Line Advection and Analysis
    IEEE Transactions on Visualization and Computer Graphics (VIS '13), 19(12):2733-2742, 2013.
    | DOI | PDF (633 KB) | Video (8.2 MB) |

  • Wenbin He, Hanqi Guo, Han-Wei Shen, and Tom Peterka
    eFESTA: Ensemble Feature Exploration with Surface Density Estimates
    IEEE Transactions on Visualization and Computer Graphics, 26(4):1716-1731, 2020.
    | DOI | PDF (1.8 MB) | GitHub |

  • Qingya Shu, Hanqi Guo, Jie Liang, Limei Che, Junfeng Liu, and Xiaoru Yuan
    EnsembleGraph: Interactive Visual Analysis of Spatialtemporal Behaviors for Ensemble Simulation Data
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis '16), Taipei, Apr. 12-15, pages 56-63, 2016.
    | DOI | PDF (4.5 MB) | Video (24 MB) |

  • Richen Liu, Hanqi Guo, Jiang Zhang, and Xiaoru Yuan
    Comparative Visualization of Vector Field Ensembles Based on Longest Common Subsequence
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis '16), Taipei, Apr. 12-15, pages 96-103, 2016.
    | DOI | PDF (5.4 MB) |

  • Wenbin He, Junpeng Wang, Hanqi Guo, Han-Wei Shen, and Tom Peterka
    CECAV: Collective Ensemble Comparison and Visualization using Deep Neural Networks
    Journal of Visual Informatics, 4(2):109--121, 2020.
    | DOI | PDF |

  • Richen Liu, Hanqi Guo, and Xiaoru Yuan
    A Bottom-Up Scheme for User-Defined Feature Comparison in Ensemble Data
    In Proceedings of SIGGRAPH Asia 2015 Symposium on Visualization in High Performance Computing, pages 10:1-10:4, Kobe, Japan, Nov. 2-5, 2015.
    | DOI | PDF |

Flow visualization

Flow visualization conveys the structure and dynamics of vector fields produced by fluid, plasma, and atmospheric simulations. Our research in this area spans particle-tracing algorithms (stochastic, parallel, and load-balanced), data-reduction strategies tailored to integral curves, comparative analysis of vector field ensembles, and learned representations of flow fields.

  • Hanqi Guo, Wenbin He, Sangmin Seo, Han-Wei Shen, Emil Mihai Constantinescu, Chunhui Liu, and Tom Peterka
    Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis
    IEEE Transactions on Visualization and Computer Graphics, 25(9):2710-2724, 2019.
    | DOI | PDF (1.8 MB) |

  • Jun Han, Jun Tao, Hao Zheng, Hanqi Guo, Danny Z. Chen, and Chaoli Wang
    Flow Field Reduction via Reconstructing Vector Data from 3D Streamlines Using Deep Learning
    IEEE Computer Graphics and Applications, Special Issue on Visual Computing with Deep Learning, 39(4):54-67, 2019.
    | DOI | PDF |

  • Hanqi Guo, Xiaoru Yuan, Jian Huang, and Xiaomin Zhu
    Coupled Ensemble Flow Line Advection and Analysis
    IEEE Transactions on Visualization and Computer Graphics (VIS '13), 19(12):2733-2742, 2013.
    | DOI | PDF (633 KB) | Video (8.2 MB) |

  • Jiang Zhang, Hanqi Guo, Fan Hong, Xiaoru Yuan, and Tom Peterka
    Dynamic Load Balancing Based on Constrained K-D Tree Decomposition for Parallel Particle Tracing
    IEEE Transactions on Visualization and Computer Graphics (VIS '17), 24(1):954-963, 2018.
    | DOI | PDF (3.5 MB) | Video Preview |

  • Jiang Zhang, Hanqi Guo, Xiaoru Yuan, and Tom Peterka
    Dynamic Data Repartitioning for Load-Balanced Parallel Particle Tracing
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis '18), pages 86-95, Kobe, Japan, April 10-13, 2018.
    | DOI | PDF (4.0 MB) |

  • Fan Hong, Chongke Bi, Hanqi Guo, Kenji Ono, and Xiaoru Yuan
    Compression-based Integral Curve Data Reuse Framework for Flow Visualization
    Journal of Visualization, 20(4):859-874, 2017.
    | DOI | PDF |

  • Richen Liu, Hanqi Guo, and Xiaoru Yuan
    User-Defined Feature Comparison for Vector Field Ensembles
    Journal of Visualization, 20(2):217-229, 2017.
    | DOI | PDF |

  • Richen Liu, Hanqi Guo, Jiang Zhang, and Xiaoru Yuan
    Comparative Visualization of Vector Field Ensembles Based on Longest Common Subsequence
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis '16), Taipei, Apr. 12-15, pages 96-103, 2016.
    | DOI | PDF (5.4 MB) |

  • Fan Hong, Chufan Lai, Hanqi Guo, Enya Shen, Xiaoru Yuan, and Sikun Li
    FLDA: Latent Dirichlet Allocation Based Unsteady Flow Analysis
    IEEE Transactions on Visualization and Computer Graphics (VIS '14), 20(12):2545-2554, 2014.
    | DOI | PDF (4.6 MB) |

  • Hanqi Guo, Fan Hong, Qingya Shu, Jiang Zhang, Jian Huang, and Xiaoru Yuan
    Scalable Lagrangian-based Attribute Space Projection for Multivariate Unsteady Flow Data
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis 2014), pages 33-40, Yokohama, Japan, Mar. 4-7, 2014.
    | DOI | PDF (1.6 MB) | Video (5.1 MB) |

Volume visualization

Volume visualization renders 3D scalar and multivariate fields directly to reveal the interior structure of scientific data. We have developed the WYSIWYG (What You See Is What You Get) family of methods, which lets users specify what they want to see by directly manipulating the rendered image, together with high-dimensional transfer-function design schemes based on dimension projection and parallel coordinates, with applications in biomedical imaging and seismic interpretation.

  • Hanqi Guo, Ningyu Mao, and Xiaoru Yuan
    WYSIWYG (What You See is What You Get) Volume Visualization
    IEEE Transactions on Visualization and Computer Graphics (VIS '11), 17(12):2106-2114, 2011.
    | DOI | PDF (1.4 MB) | Video (6.3 MB) |

  • Jun Tao, Martin Imre, Chaoli Wang, Nitesh V. Chawla, Hanqi Guo, Gökhan Sever, and Seung Hyun Kim
    Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps
    IEEE Transactions on Visualization and Computer Graphics (VIS' 18), 25(1):1236-1245, 2019.
    | DOI | PDF (7.0 MB) | Video Preview | GitHub |

  • Hanqi Guo, He Xiao, and Xiaoru Yuan
    Scalable Multivariate Volume Visualization and Analysis based on Dimension Projection and Parallel Coordinates
    IEEE Transactions on Visualization and Computer Graphics, 18(9):1397-1410, 2012.
    | DOI | PDF (860 KB) |

  • Hanqi Guo, He Xiao, and Xiaoru Yuan
    Multi-Dimensional Transfer Function Design based on Flexible Dimension Projection Embedded in Parallel Coordinates
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis 2011), pages 19-26, Hong Kong, Mar 1-4, 2011.
    | DOI | PDF (1.1 MB) | Video (4.9 MB) |

  • Hanqi Guo, Wei Li, and Xiaoru Yuan
    Transfer Function Map
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis 2014), Notes Paper, pages 262-266, Yokohama, Japan, Mar. 4-7, 2014.
    | DOI | PDF (218 KB) |

  • Hanqi Guo and Xiaoru Yuan
    Local WYSIWYG Volume Visualization
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis 2013), pages 65-72, Sydney, NSW, Australia, Feb. 28-Mar.1, 2013.
    | DOI | PDF (633 KB) | Video (8.2 MB) |

  • Richen Liu, Hanqi Guo and Xiaoru Yuan
    Seismic Structure Extraction Based on Multi-scale Sensitivity Analysis
    Journal of Visualization, 17(3):157-166, 2014
    | DOI | PDF | Video |

  • Hanqi Guo, Xiaoru Yuan, Jie Liu, Guihua Shan, Xuebin Chi, and Fei Sun
    Interference Microscopy Volume Illustration for Biomedical Data
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis 2012), pages 177-184, Songdo, Korea, Feb. 28-Mar. 2, 2012.
    | DOI | PDF (933 KB) | Video (5.1 MB) |

  • Hanqi Guo and Xiaoru Yuan
    Transfer Functions for Volume Visualization: State-of-the-Art
    Journal of Computer-Aided Design and Computer Graphics, 24(10):1249-1258, 2012. (In Chinese)
    | PDF (5.0 MB) |

Visual analytics

Visual analytics combines interactive visualization with statistical and machine-learning models to help domain experts make sense of complex, heterogeneous datasets. Our work in this space develops tailored systems for analyzing supercomputer fault and resilience data (the La VALSE system, applied to Argonne's Blue Gene/Q logs), urban traffic trajectories, and seismic and ionospheric observations, with an emphasis on scalability to long time series and large event logs.

  • Hanqi Guo, Zuchao Wang, Bowen Yu, Huijing Zhao, and Xiaoru Yuan
    TripVista: Triple Perspective Visual Trajectory Analytics and Its Application on Microscopic Traffic Data at a Road Intersection
    In Proceedings of IEEE Pacific Visualization Symposium (PacificVis 2011), pages 163-170, Hong Kong, Mar 1-4, 2011.
    | DOI | PDF (1.2 MB) | Video (6.3 MB) |

  • Hanqi Guo, Sheng Di, Rinku Gupta, Tom Peterka, and Franck Cappello
    La VALSE: Scalable Log Visualization for Fault Characterization in Supercomputers
    In Proceedings of EuroGraphics Symposium on Parallel Graphics and Visualization (EGPGV '18), pages 91-100, Brno, Czech Republic, June 4, 2018.
    | DOI | PDF (2.8 MB) | Video (24 MB) | GitHub | Highlight |

  • Fan Hong, Siming Chen, Hanqi Guo, Xiaoru Yuan, Jian Huang, and Yongxian Zhang
    Visual Exploration of Ionosphere Disturbances for Earthquake Research
    In SA'17: Proceedings of SIGGRAPH Asia 2017 Symposium on Visualization, pages 2:1-2:8, Bangkok, Thailand, November 27-30, 2017.
    | DOI | PDF |

  • Sheng Di, Hanqi Guo, Rinku Gupta, Eric R. Pershey, Marc Snir, and Franck Cappello
    Exploring Properties and Correlations of Fatal Events in a Large-Scale HPC System
    IEEE Transactions on Parallel and Distributed Systems, 30(2):361-374, 2019.
    | DOI | PDF | Press Release |

  • Dingwen Tao, Sheng Di, Hanqi Guo, Zizhong Chen, and Franck Cappello
    Z-checker: A Framework for Assessing Lossy Compression of Scientific Data
    International Journal of High Performance Computing Applications, 33(2):285-303, 2019.
    | DOI | arXiv | PDF |

  • Sheng Di, Hanqi Guo, Eric R. Pershey, Marc Snir, and Franck Cappello
    Characterizing and Understanding HPC Job Failures over The 2K-day Life of IBM BlueGene/Q System
    In Proceedings of IEEE/IFIP International Conference on Dependable Systems and Networks (DSN 2019), pages 473-484, Portland, OR, 2019.
    | DOI | PDF |

  • Xiaoru Yuan, He Xiao, Hanqi Guo, Peihong Guo, Wesley Kendall, Jian Huang, and Yongxian Zhang
    Scalable Multi-variate Analytics of Seismic and Satellite-based Observational Data
    IEEE Transactions on Visualization and Computer Graphics (VIS '10), 16(3):1413-1420, 2010.
    | DOI | PDF (3.3 MB) |