2020:

• H. Liu. & H. Zhang. A Suggestive Interface for Untangling Mathematical Knots. IEEE Transactions on Visualization and Computer Graphics (07 October 2020 ).
• H. Zhang & H. Liu. Relaxing topological surfaces in four dimensions. The Visual Computer (2020).
• J. Lin & H. Zhang. Accelerating visual communication of mathematical knot deformation. Journal of Visualization (2020).

2019:

• J. Lin & H. Zhang. View Selection in Knot Deformation. 2019 IEEE International Conference on Big Data (Big Data), 3365-3372.
• G. Ruan & H. Zhang. Parallelized Topological Relaxation Algorithm. 2019 IEEE International Conference on Big Data (Big Data), 3406-3415.
• J. Lin & H. Zhang. Visually Communicating Mathematical Knot Deformation. Proceedings of the 12th International Symposium on Visual Information Communication and Interaction, pp 1-8.
• J. Lin & H. Zhang. Visualizing Mathematical Knot Equivalence. In Proceedings of IS&T International Symposium on Electronic Imaging 2019.

2018:

• M Ando, M Fontana, GJ Eckert, RA Arthur, H Zhang, & DT Zero. Objective and quantitative assessment of caries lesion activity.Journal of dentistry 78, 76-82.
• J. Lin & H. Zhang. Untangling Mathematical Knots with Simulated Annealing and Opposition-Based Learning.2018 IEEE International Conference on Big Data (Big Data), 4245-4253.

2017:

• G. Ruan, & H. Zhang. Closed-loop big data analysis with visualization and scalable computing.Journal of Big Data Research 8, 12-26.
• R. Chen & H. Zhang. Large-scale 3D Reconstruction with an R-based Analysis Workflow.In Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, 85-93.

2016:

• J., Lin, D. Zhong, Y. Zhong, & H. Zhang. Accelerating mathematical knot simulations with R on the web.2016 IEEE International Conference on Big Data (Big Data), 2315-2321.
• H. Zhang, Y. Zhong, J. Jiang. Visualizing Knots and Braids with Touchable 3D Manipulatives.2016 IEEE Pacific Visualization Symposium (PacificVis), 24-31.

2014:

• H. Zhang, J. Weng, & G. Ruan. Visualizing 2-dimensional manifolds with curve handles in 4D. IEEEVIS/SciVis 2014. Paris, France. IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol 20, Issue 12. (In Press)
• G. Ruan, & H. Zhang. Visual Analysis of Large Dental Imaging Data in Caries Research. In Large Data Analysis and Visualization (LDAV), 2014 IEEE 4th Symposium on , vol., no., pp.77-84, 9-10 Nov. 2014
• J. Weng, G. Ruan, & H. Zhang. Exploiting Image Processing and Geometric Analysis in Carious Lesion Assessment.In Proceedings of International Conference on Internet Multimedia Computing and Service (ACM ICIMCS'14). ACM, New York, NY, USA.

2013:

J. Weng, & H. Zhang. Perceptualization of geometry using intelligent haptic and visual sensing.Proc. SPIE 8654, Visualization and Data Analysis 2013, 86540E (February 4, 2013); doi:10.1117/12/2002536.

2012:

• H. Zhang, C. Frend, & M. J. Boyles. Computer Interfaces Transform Mathematical Studies. Invited SPIE Newsroom Tech. Article. doi:10.1117/2.1201209.004497. 23 October 2012.
• H. Zhang, J. Weng, L. Jing, & Y. Zhong. KnotPad: Visualizing and Exploring Knot Theory with Fluid Reidemeister Moves. VisWeek/SciVis 2012. Seattle, WA. IEEE Transactions on Visualization and Computer Graphics (TVCG), Volume 18, Issue 12. (Dec 2012). pp 2051-2060
• L. Jing, X. Huang, Y. Zhong, Y. Wu, & H. Zhang. A Python based 4D Visualization Environment. IJACT: International Journal of Advancements in Computing Technology, Vol. 4, No. 16, pp. 460 ~ 469, 2012.
• L. Jing, X. Huang, Y. Zhong, Y. Wu, & H. Zhang. A Python based 4D Visualization Environment. ICICA 2012, LNCS 7473, pp. 421-428, 2012.
• J. Weng, & H. Zhang. Pseudo-haptic Interaction with Knot Diagrams. Journal of Electronic Imaging (SPIE JEI) 21, 033008 (Jul 2012).

2011:

• H. Zhang, J. Weng, & A. Hanson. A Pseudo-haptic Knot Diagram Interface. Proc. SPIE 7868, Visualization and Data Analysis 2011, 786807 (January 24, 2011. San Francisco, California. January 23, 2011.

2009:

• H. Zhang. Physically Interacting with Four Dimensions. PhD Dissertation (2009). Indiana University.

2007:

• H. Zhang, & A. Hanson. Shadow Driven Haptic 4D Visualization. IEEE TVCG November/December 2007 (Vol. 13, No. 6) pp. 1688-1695.
• H. Zhang, S. Thakur, & A. Hanson. Haptic Exploration Of Mathematical Knots. The Third International Symposium on Visual Computing, Lake Tahoe, 2007. Volume 4841 of Lecture Notes in Computer Science. Springer, 2007.

2006:

• H. Zhang, & A. Hanson. Physically Interacting With Four Dimensions. The second International Symposium on Visual Computing, Lake Tahoe, 2006. Volume 4291 of Lecture Notes in Computer Science. Springer, 2006.

2005:

• A. Hanson, & H. Zhang. Multimodal Exploration Of The Fourth Dimension. In Proceedings of IEEE Visualization, pages 263-270, 2005.
• J. Weng, Y. Zhuang, & H. Zhang. Error-Bounded Solid Voxelization For Polygonal Model Based On Heuristic Seed Filling. In Proceedings of ISVC 2005. LNCS 3804, pp. 607-612. 2005.

2021:

• H. Liu. & H. Zhang. Lightning Talks of EduHPC 2021. 2021 IEEE/ACM Ninth Workshop on Education for High Performance Computing (EduHPC).

2020:

• J Lin, & H Zhang. Data Structure Visualization on the Web. 2020 IEEE International Conference on Big Data (Big Data), 3272-3279.
• GC Ruan, & H Zhang. Service Provisioning through High Level, Complexity Hiding Interfaces. 2020 IEEE International Conference on Big Data (Big Data), 3288-3294.

2019:

• R Subramanian, & H Zhang. Parallel R Computing on the Web. 2019 IEEE International Conference on Big Data (Big Data), 3416-3423.
• R Subramanian, & H Zhang. Automatic code parallelization for data-intensive computing in multicore systems. Journal of Physics: Conference Series 1411 (1), 012014.
• R Subramanian, & H Zhang. Parallel Framework for Data-Intensive Computing with XSEDE. PEARC '19: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning)July 2019 Article No.: 114.
• R. Chen & H. Zhang. Large-scale 3D Reconstruction with an R-based Analysis Workflow.In Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, 85-93.

2018:

• R. Subramanian, & H. Zhang. Performance Analysis of Divide-and-Conquer strategies for Large scale Simulations in R.2018 IEEE International Conference on Big Data (Big Data), 4261-4267.
• M. Ando, M. Fontana, G.J. Eckert, R.A. Arthur, H. Zhang, & D.T. Zero. Objective and quantitative assessment of caries lesion activity. Journal of dentistry 78, 76-82.
• H. Zhang.Performance Engineering for Scientific Computing with R.International Journal on Data Science and Technology 4 (2), 42-48.

2017:

• H. Zhang, Y. Zhong, & J. Lin. Divide-and-conquer strategies for large-scale simulations in R. 2017 IEEE International Conference on Big Data (Big Data), 3517-3523.
• R. Chen & H. Zhang. Large-scale 3D Reconstruction with an R-based Analysis Workflow.In Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, 85-93.

2016:

• J Zou, & H Zhang. High-frequency financial statistics through high-performance computing. Conquering Big Data with High Performance Computing, 233-252.

2015:

• H. Zhang, R. Chen, G. Ruan, and M. Ando. Scalable dental computing on cyberinfrastructure. Big Data 2015: 2470-2478.
• W. Luo, and H. Zhang. Visual analysis of large-scale LiDAR point clouds. Big Data 2015: 2487-2492.

2014:

• G. Ruan, H. Zhang, & B. Plale. Parallelizing DBSCAN Algorithm with Distributed Iterative Graph Processing To appear in Proceedings of Workshop on Big Data Analytics: Challenges and Opportunities (BDAC-14), in conjunction with ACM/IEEE SC14.
• G. Ruan, H. Zhang, & B. Plale. Parallel and Quantitative Sequential PatternMining for Largescale Interval-based Temporal Data. In Proceedings of Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH'14), in conjunction with IEEE BigData'14.
• J. Zou, & H. Zhang. High-Frequency Financial Statistics with Parallel R and Intel Xeon Phi Coprocessor. To appear in Proceedings of Workshop on Advances in Software and Hardware for Big Data to Knowledge (ASH'14), in conjunction with IEEE BigData'14.
• G. Ruan, H. Zhang, E. Wernert, & B. Plale. TextRWeb: Large-Scale Text Analytics with R on the Web.In Proceedings of the 2014 XSEDE Conference. ACM, New York, NY, USA.
• J. Yan, H. Zhang, L. Du, E. Wernert, A. Saykin, & L. Shen. Accelerating Sparse Canonical Correlation Analysis for Large Brain Imaging Genetics Data.In Proceedings of XSEDE'14. ACM, New York, NY, USA.
• H. Zhang, W. Xu, H. Li, Y. Khamra, N. Gaffney, & E. Wernert.On comparison of R performance with hardware enabled parallelism.XSEDE'14. Atlanta, GA, USA.
• M. Ando, J. Sotomil, H. Zhang. Visualizing and Correlating Fluorescence and Microfocus Computed Tomograph (uCT) Images of White-spot Lesions. The 61th Congress of the European Organisation for Caries Research (July 2014). Greifswald, Germany.
• M. Ando, T. Sakagami, H. Zhang, G.J. Eckert, D. Zero. Evaluation of Natural Non-cavitated Caries Lesions for Severity and Activity. AADR Annual Meeting & Exhibition (March 19-22, 2014), Charlotte, NC, USA.

2013:

• Y. El-Khamra, N. Gaffney, D. Walling, E. Wernert, W. Xu, H. Zhang. Performance Evaluation of R with Intel Xeon Phi Coprocessor. In Proceedings of the First Workshop on Benchmarks, Performance Optimization, and Emerging hardware of Big Data Systems and Applications (BPOE 2013), in conjunction with 2013 IEEE International Conference on Big Data (IEEE Big Data 2013). October 8, 2013, Silicon Valley, CA, USA.
• H. Zhang, G. Ruan, H. Shen, M. Bolyes, H. Li, M. Ando. XSEDE-Enabled High-Throughput Lesion ActivityAssessment. In Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery(XSEDE '13). ACM, New York, NY, USA, Article 10.

• G. Ruan, H. Zhang, B. Plale. Exploiting MapReduce and Data Compression for Data-intensive Applications. In Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery (XSEDE '13). ACM, New York, NY, USA, Article 38.

• M. Ando, E.K. Kohara, R.A. Arthur, H. Zhang, G.J. Eckert, M. Fontana, D.T. Zero. Quantitative and Qualitative Assessment of Caries Lesion Activity. IADR 2013. Seattle, WA.

2012:

• H. Zhang, M. Ando, & M. J. Boyles. 3D-Time Series Analysis of Caries Lesion Activity for Oral Health Care. In Proceedings of the Workshop on Visual Analytics in Healthcare (VAHC 2012). (Inconjunction with the IEEE VisWeek 2012, Oct 17th, Seattle, WA.)
• H. Zhang, H., Li, M. J. Boyles, R. Henschel, E.K. Kohara, M. Ando. Exploiting HPC Resources for the 3D-Time Series Analysis of Caries Lesion Activity. In Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond (XSEDE'12). ACM, New York, NY, USA, , Article 19 , 8 pages.
• E.K. Kohara, H. Zhang, R. Arthur, G. Eckert, & M. Ando. Non-Destructive Analysis of Artificial Caries Lesions in Human Enamel. 2012 IUSD Research Day Presentation.

2019:

• Y Zhong, L Wang, M Lin, & H Zhang. Discrete pigeon-inspired optimization algorithm with Metropolis acceptance criterion for large-scale traveling salesman problem. Swarm and Evolutionary Computation 48, 134-144.

2018:

• Y. Zhong, J. Lin, L. Wang, H. Zhang. Discrete comprehensive learning particle swarm optimization algorithm with Metropolis acceptance criterion for traveling salesman problem. Swarm and Evolutionary Computation 42, 77-88.
• J. Lin, Y. Zhong, E. Li, X. Lin, & H. Zhang. Multi-agent simulated annealing algorithm with parallel adaptive multiple sampling for protein structure prediction in ab off-lattice model. Applied Soft Computing 62, 491-503.

2017:

• Y. Zhong, J. Lin, H. Zhang. Hybrid discrete artificial bee colony algorithm with threshold acceptance criterion for traveling salesman problem. Information Sciences 421, 70-84.
• S. Zhan, Y. Zhong, Z. Zhang, D. Zhong, & H. Zhang. Comparative analysis of selection schemes used in artificial bee colony algorithm.International Journal of Computing Science and Mathematics 8 (3), 218-227.

2016:

• Y. Zhong, C. Wang, L., M. Lin, and H. Zhang. Swarm simulated annealing algorithm with knowledge-based sampling for travelling salesman problem. IJISTA (In Press).

2015:

• C. Wang, M. Lin, Y. Zhong, and H. Zhang. Solving travelling salesman problem using multiagent simulated annealing algorithm with instance-based sampling. IJCSM 6(4): 336-353 (2015).

2014:

• C. Wang, M. Lin, Y. Zhong, & H. Zhang. Solving Traveling Salesman Problem Using Multi-agent Simulated Annealing Algorithm with Instance Based Sampling. IJCSM 2014, Vol. 5, No. 4 (in press).

2013:

• X. Lin, Y. Zhong, & H. Zhang. An enhanced firefly algorithm for function optimisation problems Int. J. of Modelling, Identification and Control. 2013 Vol 18, No.2, pp.166-173.
• Y. Zhong, J. Lin, H. Yang, & H. Zhang. Analysis of Learning-based Multi-agent Simulated Annealing Algorithm for Function Optimization Problems. International Journal of Computing Science and Mathematics (57917, in press).

2012:

• H. Zhang, M. Ando, & M. J. Boyles. 3D-Time Series Analysis of Caries Lesion Activity for Oral Health Care. In Proceedings of the Workshop on Visual Analytics in Healthcare (VAHC 2012). (In conjunction with the IEEE VisWeek 2012, Oct 17th, Seattle, WA.)
• H. Zhang, H., Li, M. J. Boyles, R. Henschel, E.K. Kohara, M. Ando. Exploiting HPC Resources for the 3D-Time Series Analysis of Caries Lesion Activity. In Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond (XSEDE '12). ACM, New York, NY, USA, , Article 19 , 8 pages.
• Y. Zhong, X. Lin, L. Wang, & H. Zhang. Fine-Grained Differential Evolution Algorithm for Multi-Dimensional Function Optimization. Advanced Science Letters, Volume 11, Number 1, May 2012, pp. 315-320.
• Y. Zhong, L. Wang, C. Wang, & H. Zhang. Multi-agent simulated annealing algorithm based on differential evolution algorithm. Int. J. Bio-Inspired Computation 2012 - Vol.4, No.4 pp.217-228.
• E.K. Kohara, H. Zhang, R. Arthur, G. Eckert, & M. Ando. Non-Destructive Analysis of Artificial Caries Lesions in Human Enamel. 2012 IUSD Research Day Presentation.
• Y. Zhong, J. Ning, & H. Zhang (2012). Multi-agent Simulated Annealing Algorithm Based on Particle Swarm Optimization Algorithm. International Journal of Computer Applications in Technology 2012 - Vol.43, No.4 pp.335-342.

2011:

• X. Lin, C. Wu, Y. Zhong, & H. Zhang. Firefly Algorithm with New Attractiveness Measurement Using Normalized Distance. Proceedings 2011 IEEE International Conference on Intelligent Computing and Intelligent Systems. Volume 2. pp 684-688.
• X. Lin, L. Wang, Y. Zhong, & H. Zhang. Differential Evolution Algorithm with Fine Evaluation Strategy for Multi-dimensional Function Optimization Problems. Volume 7002 of LNCS - Advances in Artificial Intelligence and Computational Intelligence (2011). Page 128-136. Springer Berlin / Heidelberg.

2005:

• J. Weng, Y. Zhuang, & H. Zhang. Error-Bounded Solid Voxelization For Polygonal Model Based On Heuristic Seed Filling. In Proceedings of ISVC 2005. LNCS 3804, pp. 607-612. 2005.

2002:

• H. Zhang. A VRML and Java based Virtual Environment for Chemistry Education. Master Thesis (2002). Zhejiang University, China.

2016:

• G Ruan, & H Zhang. Large-Scale Multi-Modal Data Exploration with Human in the Loop.Conquering Big Data with High Performance Computing, 253-268.
• L. Xu, H. Zhang, & C. Yu. See you see me: The role of eye contact in multimodal human-robot interaction.ACM Transactions on Interactive Intelligent Systems (TiiS) 6 (1), 2.

2014:

• C. Yu, L. Xu, Y. Zhong, S. Foster, & H. Zhang. Learning to interact and interacting to learn: active statistical learning in human-robot interaction. In Proceedings of IJCNN 2014. Beijing, China.

2013:

• L. Xu, H. Zhang, & C. Yu. Cooperative Gazing Behaviors in Multi-robot Human Interaction. Journal of Interaction Studies, Volume 14, Issues (14:3)(in press).
• H. Zhang, & M. J. Boyles. Visual exploration and analysis of human-robot interaction rules. Proc. SPIE 8654, Visualization and Data Analysis 2013, 86540Z (February 4, 2013); doi:10.1117/12.20028215.

2011:

• D. Fricker, H. Zhang, & C. Yu. Sequential Pattern Mining of Multimodal Data Streams in Social Interactions. Development and Learning (ICDL), 2011 IEEE International Conference on , vol.2, pp.1-6, 24-27 Aug. 2011.

2010:

• H. Zhang, D. Fricker, T.G. Smith, & C. Yu. Real-Time Adaptive Behaviors in Multimodal Human-Avatar Interactions. In Proceedings of ACM 12th International Conference on Multimodal Interfaces and 7th Workshop on Machine Learning for Multimodal Interaction (ICMI-MLMI 2010).
• H. Zhang, D. Fricker, & C. Yu. A Multimodal Real-Time Platform for Studying Human-Avatar Interactions. In Proceedings of 10th International Conference on Intelligent Virtual Agents (IVA 2010).
• H. Zhang, D. Fricker, & C. Yu. Modeling Real-time Multimodal Interactions with Virtual Humans. Two page abstract presented at IEEE ICDL 2010. Ann Arbor, Michigan.

2006:

• C. Yu, H. Zhang, & L. Smith. Learning Through Multimodal Interaction. In Proceedings of the 5th International Conference of Development and Learning.
• H. Zhang, C. Yu, & L. Smith. Using Virtual Humans to Study the Role of Social Cues in Learning. The annual meeting of the XVth Biennial International Conference on Infant Studies, Westin Miyako, Japan, Jun 19, 2006.
• H. Zhang, C. Yu, & L. Smith. An Interactive Virtual Reality Platform for Studying Embodied Social Interaction. In Proceedings of the CogSci06 Symposium Toward Social Mechanisms of Android Science.
• C. Yu, H. Zhang, & L. Smith. Using Virtual Humans To Study Embodied Social Interaction. In the Proceedings of CogScie/ICCS 2006.

all:

• Visualizing the Dynamic Process of Artificial Caries Lesion in Human Enamel. This visualization presents data from a research study aimed at quantifying caries lesion activity in human enamel. Dental caries is an infectious, communicable disease which causes destruction of teeth but is detectable and reversible $ computational techniques to enable quantitative comparisons of digitally-imaged lesion activity over time. Specifically, this visualization renders the caries lesions from a portion of a single human tooth at five stages of the disease imaged ove$ lesions; (2) Day 5: Artificially demineralized (diseased) state with significant lesions; (3) Day 9: Initial remineralized (beginning-to-heal) state with reduced lesions; (4) Day 15: Subsequent remineralized (further healed) state with even less $ comparable to the baseline sound enamel state indicating a successful reversal of the artificially-induced caries activity. Play Video
• Touching the 4D Torus.The 4D Torus is an example of using haptics to explore and visualize mathematical objects that exist outside of normal 3D space. In this video segment, a 4D dimensional torus (doughnut) is explored with haptics and graphics to allow researchers to feel objects that cannot be easily visualized. Play Video
• Visual Computing of Large Dental Imaging Data for Investigative Caries Studies. This visualization presents 3-dimensional complete tooth (enamel, dentin and pulp) and volumetric caries lesion data corresponding to multiple threshold levels from a research study aimed at quantifying caries lesion activity in human enamel.Caries lesion activity assessment has been a routine diagnostic procedure in caries management, traditionally employing subjective measurements incorporating visual and tactile inspections. Recently, advances in 2D/3D image processing and analysis methods and microfocus x-ray computerized tomography (m-CT) hardware, along with increased power of high performance computing (HPC), have created a synergic effect that is revolutionizing the dental computing field. This visualization produces 3-dimensional complete tooth (enamel, dentin and pulp) models from m-CT data of 9 human tooth specimens, and renders the loss of mineral content at multiple threshold levels in each tooth specimen. Our visualization has seen early use in helping answer domain-specific questions such as quantitative assessment of dynamic carious lesion activities, visual representation of tooth mineral distributions, and the mapping and correlation of caries phenomena exhibited in volume and surface imaging data.Throughout this project and in preparation for this visualization, a combination of XSEDE and non-XSEDE resources were utilized. The majority of the computational time was spent extracting contours of various dental structures and identifying the cross-section of demineralized enamel. To ensure fast turnaround time of this task, large dental imaging data are processed and interested structures (e.g., geometries that represent dental crown, pulp and dentin surface) are extracted offline with MapReduce tasks deployed at Stampede cluster machine. The geometric models were imported into either ParaView where data and render servers (also hosted on Stampede) facilitated both interactive visual exploration (i.e. qualitative analysis) and quantitative analysis or Maya for the creation of non-interactive visualization and animation. XSEDE SciVis Showcase