Publications


Papers

2020

  • 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.

  • 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.

  • 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, pages 81-90, 2020.

  • 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, Journal of Visual Informatics, 4(2):109--121, 2020.

  • 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.

  • Chad A. Steed, John R. Goodall, Junghoon Chae, and Artem Trofimov. “CrossVis: A Visual Analytics System for Exploring Heterogeneous Multivariate Data with Applications to Materials and Climate Sciences”, Graphics & Visual Computing, 3:20013, 2020.

  • Qiao Kang, Alex Sim, Peter Nugent, Sunwoo Lee, Wei-Keng Liao, Ankit Agrawal, Alok Choudhary and Kesheng Wu. “Predicting Resource Requirement in Intermediate Palomar Transient Factory Workflow”. In the 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, May 2020.

  • Nan Ding, Samuel Williams, Yang Liu, Xiaoye S. Li, "Leveraging One-Sided Communication for Sparse Triangular Solvers", 2020 SIAM Conference on Parallel Processing for Scientific Computing, February 14, 2020.

  • M. Isakov, E. Rosario, S. Madireddy, P. Balaprakash, P. Carns, R. Ross, M. Kinsy. “HPC I/O Throughput Bottleneck Analysis with Explainable Local Models.” In SC ’20: IEEE/ACM International Conference on High Performance Computing, Networking, Storage and Analysis, 2020.

  • R. Maulik, N. A. Garland, J. W. Burby, X. Tang, and P. Balaprakash. "Neural network representability of fully ionized plasma fluid model closures." Physics of Plasmas 27, no. 7 (2020): 072106.

  • R. Maulik, A. Mohan, B. Lusch, S. Madireddy, P. Balaprakash, and D. Livescu. "Time-series learning of latent-space dynamics for reduced-order model closure." Physica D: Nonlinear Phenomena 405 (2020): 132368.

  • R. Maulik, B. Lusch, and P. Balaprakash. "Non-autoregressive time-series methods for stable parametric reduced-order models." Physics of Fluids (2020).

  • Zhe Wang, Pradeep Subedi, Matthieu Dorier, Philip E. Davis, and Manish Parashar. “Staging Based Task Execution For Data Driven In-Situ Scientific Workflows”. 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2020.

  • Anjia Wang, Alok Mishra, Chunhua Liao, Yonghong Yan, and Barbara Chapman, "FreeCompilerCamp.org: Training for OpenMP Compiler Development from Cloud", Volume 11, Issue 1, pp. 53 - 60, Journal of Computational Science Education, Jan. 2020

  • Gleison Souza Diniz Mendonça, Chunhua Liao, and Fernando Magno Quintão Pereira. “AutoParBench: a Unified Test Framework for OpenMP-based Parallelizers.” In Proceedings of the 34th ACM International Conference on Supercomputing (ICS '20). Association for Computing Machinery, New York, NY, USA, Article 28, 1–10.

  • A. Lasa, J. Canik, S. Blondel, T. Younkin, D. Curreli, J. Drobny, P.C. Roth, M. Cianciosa, W. Elwasif, D. Green, B. Wirth, “Multi-physics Modeling of the Long-term Evolution of Helium Plasma Exposed Surfaces,” Physica Scripta T171:014041, January 2020

  • S.F. Siegel, Y. Yan. “Action-based Model Checking: Logic, Automata, and Reduction”. Computer Aided Verification (CAV 2020), LLNCS 12225, Springer, 77–100, 2020

  • Jacob Lambert, Seyong Lee, Jeffrey S. Vetter, and Allen D. Malony, “MPACC: An Integrated Translation and Optimization Framework for OpenACC and OpenMP”, SC 2020: The International Conference for High Performance Computing, Networking, Storage, and Analysis, 2020.

  • Roberto Gioiosa, Burcu O. Mutlu, Seyong Lee, Jeffrey S. Vetter, Giulio Picierro, and Marco Cesati, “The Minos Computing Library: Efficient Parallel Programming for Extremely Heterogeneous Systems”, Proceedings of the 13th Annual Workshop on General Purpose Processing using Graphics Processing Unit (GPGPU’20), 2020.

2019

  • 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.

  • 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, 25(1):1236-1245, 2019.

  • Junghoon Chae, Debsindhu Bhowmik, Heng Ma, Arvind Ramanathan, and Chad A. Steed. “Visual Analytics for Deep Embeddings of Large Scale Molecular Dynamics Simulations”, In Proceedings of the IEEE International Conference on Big Data, Dec. 2019.

  • Junghoon Chae, Chad A. Steed, John Goodall, and Steven Hahn. “Dynamic Color Mapping with a Multi-scale Histogram: A Design Study with Physical Scientists”, In Proceedings of the SPIE Visualization and Data Analysis Conference, Jan. 2019.

  • Artem A. Trofimov, Alison A. Pawlicki, Nikolay Borodinov, Shovon Mandal, Teresa J. Mathews, Mark Hildebrand, Maxim A. Ziatdinov, Katherine A. Hausladen, Paulina K. Urbanowicz, Chad A. Steed, Anton V. Ievlev, Alex Belianinov, Joshua K. Michener, Rama Vasudevan, and Olga S. Ovchinnikova. “Deep Data Analytics for Genetic Engineering of Diatoms Linking Genotype to Phenotype via Machine Learning”, npj Computational Materials, 5:4, 2019.

  • Sunwoo Lee, Qiao Kang, Sandeep Madireddy, Prasanna Balaprakash, Ankit Agrawal, Alok Choudhary, Richard Archibald, and Wei-keng Liao. “Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time”. In the IEEE International Conference on Big Data, December 2019.

  • Khaled Ibrahim, Samuel Williams, Leonid Oliker, "Performance Analysis of GPU Programming Models using the Roofline Scaling Trajectories", International Symposium on Benchmarking, Measuring and Optimizing (Bench), BEST PAPER AWARD, November 2019

  • Nan Ding, Samuel Williams, "An Instruction Roofline Model for GPUs", Performance Modeling, Benchmarking, and Simulation (PMBS), BEST PAPER AWARD, November 18, 2019

  • Allen D. Malony, Srinivasan Ramesh, Kevin Huck, Nicholas Chaimov, and Sameer Shende. 2019. A Plugin Architecture for the TAU Performance System. In Proceedings of the 48th International Conference on Parallel Processing (ICPP 2019). Association for Computing Machinery, New York, NY, USA, Article 90, 1–11. DOI:https://doi.org/10.1145/3337821.3337916

  • D. Boehme, K. Huck, J. Madsen and J. Weidendorfer, "The Case for a Common Instrumentation Interface for HPC Codes," 2019 IEEE/ACM International Workshop on Programming and Performance Visualization Tools (ProTools), Denver, CO, USA, 2019, pp. 33-39, doi: 10.1109/ProTools49597.2019.00010.

  • S. Madireddy, P. Balaprakash, P. Carns, R. Latham, G. K. Lockwood, R. Ross, S. Snyder, and S. M. Wild. "Adaptive Learning for Concept Drift in Application Performance Modeling." In Proceedings of the 48th International Conference on Parallel Processing, pp. 1-11. 2019.

  • J. Wang, P. Balaprakash, and R. Kotamarthi. Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast model. Geoscientific Model Development, 2019:1–31, 2019.

  • S. Madireddy, N. Li, N. Ramachandra, P. Balaprakash, and S. Habib. "Modular Deep Learning Analysis of Galaxy-Scale Strong Lensing Images." In ML and the Physical Sciences Workshop at NeurIPS, 2019.

  • R. Maulik, V. Rao, S. Madireddy, B. Lusch, and P. Balaprakash. "Using recurrent neural networks for nonlinear component computation in advection-dominated reduced-order models." In ML and the Physical Sciences Workshop at NeurIPS, 2019.

  • Wan, Lipeng, Mehta, Kshitij V., Klasky, Scott A., Wolf, Matthew D., Wang, H Y., Wang, W H., Li, J C., and Lin, Zhihong. Mon . "Data Management Challenges of Exascale Scientific Simulations: A Case Study with the Gyrokinetic Toroidal Code and ADIOS". United States. https://www.osti.gov/servlets/purl/1558473.

  • Pradeep Subedi, Philip E. Davis, and Manish Parashar. "Leveraging Machine Learning for Anticipatory Data Delivery in Extreme Scale In-situ Workflows." 2019 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 2019.

  • Yonghong Yan, Anjia Wang, Chunhua Liao, Tom Scogland and Bronis R. de Supinski, “Extending OpenMP Metadirective Semantics for Runtime Adaptation”, Fifteenth International Workshop on OpenMP (IWOMP 2019), Auckland, New Zealand, September 11–13, 2019

  • Jie Ren, Chunhua Liao and Dong Li, Opera: Data Access Pattern Similarity Analysis To Optimize OpenMP Task Affinity, 24th International Workshop On High-level Parallel Programming Models And Supportive Environments (HIPS), Held in Conjunction With 33rd IPDPS International Parallel & Distributed Processing Symposium, May 20-24, 2019, Rio De Janeiro, Brazil

  • P.C. Roth, “Improved Accuracy for Automated Communication Pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning,” In A. Bhatele, D. Boehme, J. Levine, A. Malony, M. Schulz (eds) Programming and Performance Visualization Tools, Lecture Notes in Computer Science 11027, Springer, Cham, pp. 38-55, 2019

  • P.C. Roth, K. Huck, G. Gopalakrishnan, F. Wolf, “Using Deep Learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges,” In A. Bhatele, D. Boehme, J. Levine, A. Malony, M. Schulz (eds) Programming and Performance Visualization Tools, Lecture Notes in Computer Science 11027, Springer, Cham, pp. 265-272, 2019

  • W. Elwasif, A. Lasa, P.C. Roth, T. Younkin, M. Cianciosa, “Nested Workflows for Loosely Coupled HPC Simulations,” 16th ACES/IEEE International Conference on Computer Systems and Applications (AICCSA 2019), November 2019

  • S.F. Siegel, “What’s Wrong with On-the-fly Partial Order Reduction”. Computer Aided Verification (CAV 2019), LLNCS 11562, Springer, 478–495, 2019

  • Forrest Shriver, Seyong Lee, Steven Hamilton, Jeffrey Vetter, and Justin Watson, VEXS, “An Open Platform for the Study of Continuous-Energy Neutron Transport Cross-Section Lookup Algorithms on GPUs”, MC19: International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, 2019.

  • Seyong Lee, John Gounley, Amanda Randles, and Jeffrey S. Vetter, “Performance Portability Study for Massively Parallel Computational Fluid Dynamics Application on Scalable Heterogeneous Architectures”, Journal of Parallel and Distributed Computing (JPDC), 2019.

2018

  • M. Dorier, P. Carns, K. Harms, R. Latham, R. Ross, S. Snyder, J. Wozniak, S.K. Gutierrez, B. Robey, B. Settlemyer, G. Shipman, J. Soumagne, J. Kowalkowski, M. Paterno, and S. Sehrish. Methodology for the Rapid Development of Scalable HPC Data Services. In Proceedings of the 3rd Joint International Workshop on Parallel Data Storage and Data Intensive Scalable Computing Systems, November 2018.

  • Junpeng Wang, Liang Gou, Han-Wei Shen, Hao Yang : DQNViz: A Visual Analytics Approach to Understand Deep Q-Networks, IEEE Transactions on Visualization and Computer Graphics, (Accepted at IEEE VAST 2018) [Best Paper Honorable Mention Award]

  • Junpeng Wang, Subhashis Hazarika, Cheng Li, Han-Wei Shen: Visualization and Visual Analysis of Ensemble Data: A Survey, IEEE Transactions on Visualization and Computer Graphics, 2018 (Early Access)

  • Subhashis Hazarika, Soumya Dutta, Han-Wei Shen, Jen-Peng Chen: CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data, IEEE Transactions on Visualization and Computer Graphics (IEEE SciVis 2018)

  • 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 Proceedings of 2018 IEEE Symposium on Large Data Analysis and Visualization, 2018. [Honorable Mention]

  • 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. (Accepted)

  • 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. (Accepted)

  • Subhashis Hazarika, Ayan Biswas, Han-Wei Shen: Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models, IEEE Transactions on Visualization and Computer Graphics , 24(1): 934-943 (2018)

  • 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), 2018. (Accepted)

  • P. Balaprakash, J. Dongarra, T. Gamblin, M. Hall, J. Hollingsworth, B. Norris, R. Vuduc, "Autotuning in High-Performance Computing Applications," in Proceedings of the IEEE, vol. 106, no. 11, pp. 2068-2083, Nov. 2018.

  • 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.

  • 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.

  • Hongzhang Shan, Samuel Williams, Calvin W. Johnson, "Improving MPI Reduction Performance for Manycore Architectures with OpenMP and Data Compression", Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS), November 2018.

  • Pradeep Subedi, Philip Davis, Shaohua Duan, Scott Klasky, Hemanth Kolla, and Manish Parashar. "Stacker: an autonomic data movement engine for extreme-scale data staging-based in-situ workflows." In Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), p. 73. IEEE Press, November 2018.

  • Sunwoo Lee, Ankit Agrawal, Prasanna Balaprakash, Alok Choudhary, and Wei-keng Liao, "Communication-Efficient Parallelization Strategy for Deep Convolutional Neural Network Training", the Workshop of Machine Learning in HPC Environments, held in conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis, November 2018.

  • Charlene Yang, Rahulkumar Gayatri, Thorsten Kurth, Protonu Basu, Zahra Ronaghi, Adedoyin Adetokunbo, Brian Friesen, Brandon Cook, Douglas Doerfler, Leonid Oliker, Jack Deslippe, Samuel Williams, "An Empirical Roofline Methodology for Quantitatively Assessing Performance Portability", International Workshop on Performance, Portability and Productivity in HPC (P3HPC), November 2018.

  • P.C. Roth, K. Huck, G. Gopalakrishnan, and F. Wolf, "Using Deep Learning for Automated Communication Pattern Characterization: Little Steps and Big Challenges," Fifth International Workshop on Visual Performance Analysis (VPA18), Dallas, Texas, November 2018.

  • Khaled Ibrahim, Samuel Williams, Leonid Oliker, "Roofline Scaling Trajectories: A Method for Parallel Application and Architectural Performance Analysis", HPCS Special Session on High Performance Computing Benchmarking and Optimization (HPBench), July 2018.

  • James Kress, Jong Choi, Scott Klasky, Michael Churchill, Hank Childs, and David Pugmire, "Binning Based Data Reduction for Vector Field Data of a Particle-In-Cell Fusion Simulation", ISC Workshop on In Situ Visualization (WOIV), Frankfurt, Germany, June 2018.

  • Mark Kim, James Kress, Jong Youl Choi, Norbert Podhorszki, Scott Klasky, Matthew Wolf, Kshitij Mehta, Kevin Huck, Berk Geveci, Sujin Phillip, 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", ISC Workshop on In Situ Visualization (WOIV), Frankfurt, Germany, June 2018.

  • David Pugmire, Abhishek Yenpure, Mark Kim, James Kress, Robert Maynard, Hank Childs, and Bernd Hentschel, "Performance Portable Particle Advection with VTK-m," Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), Brno, Czech Republic, June 2018.

  • Kaixi Hou, Hao Wang, Wu-chun Feng, Jeffrey S. Vetter, and Seyong Lee. Highly Efficient Compensation-based Parallelism for Wavefront Loops on GPUs. 32th IEEE International Parallel & Distributed Processing Symposium (IPDPS), Vancouver, Canada, 2018.

  • Charlene Yang, Brian Friesen, Thorsten Kurth, Brandon Cook, Samuel Williams, "Toward Automated Application Profiling on Cray Systems", Cray User Group (CUG), May 2018.

  • Penporn Koanantakool, Alnur Ali, Ariful Azad, Aydın Buluç, Dmitriy Morozov, Sang-Yun Oh, Leonid Oliker, Katherine Yelick: Communication-Avoiding Optimization Methods for Massive-Scale Graphical Model Structure Learning. 21st International Conference on Artificial Intelligence and Statistics (AISTATS), April 9 - 11, 2018, ArXiv eprints.

  • Tuomas Koskela, Jack Deslippe, Zakhar Matveev, Adetokunbo Adedoyin, Charlene Yang, Rahulkumar Gayatri, Hongzhang Shan, Zhengji Zhao, Philippe Thierry, Roman Belonov, Samuel Williams, Leonid Oliker, A Novel Multi-Level integrated Roofline Model Approach for Performance Characterization, High Performance Computing: 33rd International Conference, ISC High Performance 2018, Frankfurt, Germany, June 24–28, 2018.

  • Khaled Z Ibrahim, Samuel Williams, Leonid Oliker, Roofline Scaling Trajectories: A Method for Parallel Application and Architectural Performance Analysis, High Performance Computing Benchmarking and Optimization (HPBench 2018). As part of The 16th International Conference on High Performance Computing & Simulation (HPCS 2018), Orléans, France, July 16 – 20, 2018.

  • P.C. Roth, "Scalable, Automated Characterization of Parallel Application Communication Behavior," (presentation), 2018 Scalable Tools Workshop, Solitude, Utah, July 2018.

  • Subhashis Hazarika, Ayan Biswas, Han-Wei Shen: Uncertainty Visualization Using Copula-Based Analysis in Mixed Distribution Models, IEEE Transactions on Visualization and Computer Graphics , 24(1): 934-943 (2018)

  • Ko-Chih Wang, Naeem Shareef, and Han-Wei Shen: Image and Distribution Based Volume Rendering for Large Data Sets, IEEE PacificVis 2018

  • Junpeng Wang, Liang Gou , Hao Yang, and Han-Wei Shen: GANViz: A Visual Analytics Approach to Understand the Adversarial Game, IEEE Trans. Vis. Comput. Graph. [IEEE PacificVis 2018 Best Paper Award]

  • Tzu-Hsuan Wei, Soumya Dutta, and Han-Wei Shen: Information Guided Data Sampling and Recovery using Bitmap Indexing, IEEE PacificVis 2018

  • Soumya Dutta, Han-Wei Shen, and Jen-Ping Chen: In Situ Prediction Driven Feature Analysis in Jet Engine Simulations, IEEE PacificVis 2018

  • Cheng Li, Joachim Moortgat, and Han-Wei Shen: An Automatic Data Deformation Approach for Occlusion Free Egocentric Data Exploration, IEEE PacificVis 2018

  • S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and S. M. Wild, Modeling I/O Performance Variability Using Conditional Variational Auto Encoders, preprint ANL/MCS-P9070-0518, 2018.

  • S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and S. M. Wild, Machine Learning Based Parallel I/O Predictive Modeling: A Case Study on Lustre File Systems, In: Yokota R., Weiland M., Keyes D., Trinitis C. (eds) High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science, vol 10876. Springer, Cham, doi:10.1007/978-3-319-92040-5_10.

  • X Xing, B Dong, J Ajo-Franklin, and Kesheng Wu. Automated Parallel Data Processing Engine with Application to Large-Scale Feature Extraction. In: Machine Learning in HPC Environment. Nov. 2018. https://sc18.supercomputing.org/proceedings/workshops/workshop_pages/ws_mlhpce109.html

  • J Gu, S Klasky, N Podhorszki, J Qiang, K Wu. Querying Large Scientific Data Sets with Adaptable IO System ADIOS. Asian Conference on Supercomputing Frontiers, 2018. pp. 51-69.

  • P.C. Roth, "Improved Accuracy for Automated Communication Pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning," Proceedings of the 6th Workshop on Extreme-Scale Programming Tools (ESPT'17), Denver, Colorado, USA, Nov. 2017. To be published in Lecture Notes in Computer Science 11027, 2018.

2017

  • Cheng Li, Han-Wei Shen: Winding Angle Assisted Particle Tracing in Distribution-Based Vector Field, SIGGRAPH Asia Symposium on Visualization 2017. [Best Paper Honorable Mention award]

  • Soumya Dutta, Xiaotong Liu, Ayan Biswas, Han-Wei Shen, and Jen-Ping Chen: Pointwise Information Guided Visual Analysis of Time-varying Multi-fields, SIGGRAPH Asia Symposium on Visualization 2017

  • Junpeng Wang, Xiaotong Liu, Han-Wei Shen: High-dimensional data analysis with subspace comparison using matrix visualization, Information Visualization 2017

  • Junpeng Wang, Xiaotong Liu, Han-Wei Shen, Guang Lin: Multi-Resolution Climate Ensemble Parameter Analysis with Nested Parallel Coordinates Plots. IEEE Trans. Vis. Comput. Graph. 23(1): 81-90 (2017)

  • S. Madireddy, P. Balaprakash, P. Carns, R. Latham, R. Ross, S. Snyder, and S. M. Wild. Analysis and Correlation of Application I/O Performance and System-Wide I/O Activity, The 12th International Conference on Networking, Architecture, and Storage, Shenzhen, 2017, pp. 1-10. doi:10.1109/NAS.2017.8026844.

  • Sang-Yun Oh, Alnur Ali, Penporn Koanantakool, Ariful Azad, Aydın Buluç, Dmitriy Morozov, Leonid Oliker, Katherine Yelick: Whole-brain Functional Connectivity Mapping and Region Segmentation from Distributed Estimation of Voxel-level Sparse Precision Matrix. BigNeuro Workshop @ Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, Dec 9, 2017.

Posters

2018