Bei Wang portrait
  • Adjunct Associate Professor, Mathematics
  • Associate Professor, School Of Computing
801-585-0968

Publications

  • Qingsong Wang, Guanquan Ma, Raghavendra Sridharamurthy, Bei Wang (2024). Measure-Theoretic Reeb Graphs and Reeb Spaces. International Symposium on Computational Geometry (SOCG). Published, 06/11/2024.
  • Xinyuan Yan, Youjia Zhou, Arul Mishra, Himanshu Mishra, Bei Wang (2024). Exploring Visualization for Fairness in AI Education. IEEE Pacific Visualization Symposium (PacificVis). Published, 04/23/2024.
  • Mengjiao Han, Sudhanshu Sane, Jixian Li, Shubham Gupta, Bei Wang, Steve Petruzza, Chris R. Johnson. (2024). Interactive Visualization of Time-Varying Flow Fields Using Particle Tracing Neural Networks. IEEE Pacific Visualization Symposium (PacificVis). Published, 04/23/2024.
  • Lin Yan, Hanqi Guo, Tom Peterka, Bei Wang, Jiali Wang (2023). TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclone. IEEE Transactions on Visualization and Computer Graphics. Vol. 30, 1249-1259. Published, 11/06/2023.
  • Lin Yan, Xin Liang, Hanqi Guo, Bei Wang (2023). TopoSZ: Preserving Topology in Error-Bounded Lossy Compression. IEEE Transactions on Visualization and Computer Graphics. Vol. 30, 1302-1312. Published, 11/06/2023.
    https://www.doi.org/10.1109/TVCG.2023.3326920
  • Youjia Zhou & Helen Jenne, Davis Brown, Madelyn Shapiro, Brett Jefferson, Cliff Joslyn, Gregory Henselman-Petrusek, Brenda Praggastis, Emilie Purvine, Bei Wang (2023). Comparing Mapper Graphs of Artificial Neuron Activations. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS. 41-50. Published, 10/22/2023.
  • Mustafa Hajij, Bei Wang, Paul Rosen (2023). Homology-Preserving Multi-Scale Graph Skeletonization Using Mapper on Graphs. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS. 10-20. Published, 10/22/2023.
  • Youjia Zhou, Janis Lazovskis, Michael J. Catanzaro, Matthew Zabka, Bei Wang (2023). Combinatorial Exploration of Morse-Smale Functions on the Sphere via Interactive Visualization. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS. 51-60. Published, 10/22/2023.
  • Mingzhe Li, Sourabh Palande, Lin Yan, Bei Wang (2023). Sketching Merge Trees for Scientific Visualization. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS. 61-71. Published, 10/22/2023.
  • Youjia Zhou, Arul Mishra, Himanshu Mishra, Bei Wang (2023). From Flowchart to Questionnaire: Increasing Access to Justice via Visualization. IEEE Workshop on Visualization for Social Good (VIS4Good). 11-15. Published, 10/22/2023.
  • Mingzhe Li, Carson Storm, Austin Yang Li, Tom Needham, Bei Wang (2023). Comparing Morse Complexes Using Optimal Transport: An Experimental Study. IEEE Visualization and Visual Analytics (VIS) Short Paper. 41-45. Published, 10/21/2023.
  • Samir Chowdhury, Tom Needham, Ethan Semrad, Bei Wang, Youjia Zhou (2023). Hypergraph Co-Optimal Transport: Metric and Categorical Properties. Journal of Applied and Computational Topology. Published, 09/30/2023.
  • Chedi Morchdi, Yi Zhou, Jie Ding, Bei Wang (2023). Exploring Gradient Oscillation in Deep Neural Network Training. 59th Annual Allerton Conference on Communication, Control, and Computing. Published, 09/27/2023.
  • Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang (2023). Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. Topology, Algebra, and Geometry in Machine Learning (TAGML) Workshop at ICML. Published, 07/28/2023.
  • Nate Clause, Tamal K. Dey, Facundo Mémoli & Bei Wang (2023). Meta-diagrams for 2-parameter persistence. International Symposium on Computational Geometry (SOCG). Published, 06/12/2023.
  • Archit Rathore & Sunipa Dev, Jeff M. Phillips, Vivek Srikumar, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei Zhang, Bei Wang (2023). VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations. ACM Transactions on Interactive Intelligent Systems. Vol. 14, 1-34. Published, 05/02/2023.
  • Archit Rathore, Yichu Zhou, Vivek Srikumar, Bei Wang (2023). TopoBERT: Exploring the Topology of Fine-Tuned Word Representations. Information Visualization. Vol. 22, 186-208. Published, 05/01/2023.
  • Lin Yan, Paul Aaron Ullrich, Luke P. Van Roekel, Bei Wang, Hanqi Guo (2023). Multilevel Robustness for 2D Vector Field Feature Tracking, Selection, and Comparison. Computer Graphics Forum. Vol. 42, e14799. Published, 04/13/2023.
  • Jordan A. Berg & Youjia Zhou, Yeyun Ouyang, Ahmad A. Cluntun, T. Cameron Waller, Megan E. Conway, Sara M. Nowinski, Tyler Van Ry, Ian George, James E. Cox, Bei Wang, Jared Rutter (2023). Metaboverse Enables Automated Discovery and Visualization of Diverse Metabolic Regulatory Patterns. Nature Cell Biology. Vol. 25, 616-625. Published, 04/03/2023.
  • Kevin G. Hicks & Ahmad A. Cluntun et al, Youjia Zhou, Bei Wang, Jared Rutter (2023). Protein-Metabolite Interactomics of Carbohydrate Metabolism Reveal Regulation of Lactate Dehydrogenase. Science. Vol. 379, 996-1003. Published, 03/09/2023.
  • Emilie Purvine, Davis Brown, Brett Jefferson, Cliff Joslyn, Brenda Praggastis, Archit Rathore, Madelyn Shapiro, Bei Wang & Youjia Zhou (2023). Experimental Observations of the Topology of Convolutional Neural Network Activations. Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023. Published, 02/07/2023.
  • Gabrielius A. Kudirka, Xinyuan Yan, Sarah Kunzler, Yirong Zhou, Bei Wang & Xiaoyue Cathy Liu (2023). Enable Decision Making for Battery Electric Bus Deployment Using Robust High-Resolution Interdependent Visualization. Transportation Research Board (TRB) 102nd Annual Meeting 2023. Published, 01/08/2023.
  • Fangfei Lan, Sourabh Palande, Michael Young & Bei Wang (2022). Uncertainty Visualization for Graph Coarsening. IEEE International Conference on Big Data (IEEE BigData), Workshop on Graph Techniques for Adversarial Activity Analytics (GTA3), 2022. Published, 12/17/2022.
  • Bei Wang, Arul Mishra & Himanshu Mishra (2022). Humans as Mitigators of Biases in Risk Prediction via Field Studies. IEEE International Conference on Big Data (IEEE BigData), Workshop on Responsible AI and Data Ethics (RAIDE), 2022. Published, 12/17/2022.
  • Bhavana Doppalapudi, Bei Wang & Paul Rosen (2022). Untangling Force-Directed Layouts Using Persistent Homology. Topological Data Analysis and Visualization (TopoInVis) 2022. Published, 10/17/2022.
    https://doi.org/10.1109/TopoInVis57755.2022.00015
  • Daniel Klötzl, Tim Krake, Youjia Zhou, Jonathan Stober, Kathrin Schulte, Ingrid Hotz, Bei Wang & Daniel Weiskopf (2022). Reduced Connectivity for Local Bilinear Jacobi Sets. Topological Data Analysis and Visualization (TopoInVis) 2022. Published, 10/17/2022.
    https://doi.org/10.1109/TopoInVis57755.2022.00011
  • Daniel Klötzl, Tim Krake, Youjia Zhou, Ingrid Hotz, Bei Wang & Daniel Weiskopf (2022). Local Bilinear Computation of Jacobi Sets. The Visual Computer. Vol. 38, 3435-3448. Published, 06/30/2022.
    https://doi.org/10.1007/s00371-022-02557-4
  • Lin Yan, Talha Bin Masood, Farhan Rasheed, Ingrid Hotz & Bei Wang (2022). Geometry-Aware Merge Tree Comparisons for Time-Varying Data with Interleaving Distances. IEEE Transactions on Visualization and Computer Graphics. Published, 03/29/2022.
    https://doi.org/10.1109/TVCG.2022.3163349
  • Youjia Zhou, Nathaniel Saul, Ilkin Safarli, Bala Krishnamoorthy & Bei Wang (2022). Stitch Fix for Mapper and Topological Gains. (pp. 265-294). Vol. 30, Research in Computational Topology 2, Association for Women in Mathematics Series. Published, 01/27/2022.
    https://doi.org/10.1007/978-3-030-95519-9_12
  • Ana Lucia Garcia-Pulido, Kathryn Hess, Jane Tan, Katharine Turner, Bei Wang & Naya Yerolemou (2022). Graph Pseudometrics from a Topological Point of View. (pp. 99-128). Vol. 30, Research in Computational Topology 2, Association for Women in Mathematics Series. Published, 01/27/2022.
    https://doi.org/10.1007/978-3-030-95519-9_5
  • Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang & Lori Ziegelmeier (2022). Local Versus Global Distances for Zigzag Persistence Modules. (pp. 265-294). Vol. 30, Research in Computational Topology 2, Association for Women in Mathematics Series. Published, 01/27/2022.
    https://doi.org/10.1007/978-3-030-95519-9_3
  • Archit Rathore, Sunipa Dev, Jeff Phillips, Vivek Srikumar, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei Zhang & Bei Wang (2021). An interactive visual demo of bias mitigation techniques for word representations. Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, PMLR. Vol. 176, 330-334. Published, 12/14/2021.
  • Archit Rathore & Sunipa Dev, Jeff Phillips, Vivek Srikumar, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei Zhang, Bei Wang. (2021). An Interactive Visual Demo of Bias Mitigation Techniques for Word Representations From a Geometric Perspective. Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, PMLR. Vol. 176, 330-334. Published, 12/06/2021.
  • Youjia Zhou, Nithin Chalapathi, Archit Rathore, Yaodong Zhao & Bei Wang (2021). Mapper Interactive: A scalable, extendable, and interactive toolbox for the visual exploration of high-dimensional data. IEEE Pacific Visualization Symposium. Published, 04/19/2021.
  • Avishan Bagherinezhad, Michael Young, Bei Wang & Masood Parvania (2021). Spatio-temporal visualization of interdependent battery bus transit and power distribution systems. IEEE PES Innovative Smart Grid Technologies Conference. Published, 02/16/2021.
  • Archit Rathore, Nithin Chalapathi, Sourabh Palande & Bei Wang (2021). TopoAct: Visually exploring the shape of activations in deep learning. Computer Graphics Forum. Published, 01/13/2021.
    https://doi.org/10.1111/cgf.14195
  • Adam Brown, Omer Bobrowski, Elizabeth Munch & Bei Wang (2020). Probabilistic convergence and stability of random mapper graphs. Journal of Applied and Computational Topology. Vol. 5, 99-140. Published, 12/17/2020.
    https://doi.org/10.1007/s41468-020-00063-x
  • Tushar Athawale, Dan Maljovec, Lin Yan, Chris R. Johnson, Valerio Pascucci & Bei Wang (2020). Uncertainty visualization of 2D Morse complex ensembles using statistical summary maps. IEEE Transactions on Visualization and Computer Graphics. Vol. 28, 1955-1966. Published, 09/08/2020.
    https://doi.org/10.1109/TVCG.2020.3022359
  • Roxana Bujack, Lin Yan, Ingrid Hotz, Christoph Garth & Bei Wang (2020). State of the art in time-dependent flow topology: Interpreting physical meaningfulness through mathematical properties. Computer Graphics Forum. Published, 07/18/2020.
    https://doi.org/10.1111/cgf.14037
  • Youjia Zhou, Kevin Knudson & Bei Wang (2020). Visual demo of discrete stratified Morse theory (media exposition). International Symposium on Computational Geometry. Published, 06/29/2020.
    https://doi.org/10.4230/LIPIcs.SoCG.2020.82
  • Michael J. Catanzaro (2020). Moduli spaces of Morse functions for persistence. Journal of Applied and Computational Topology. Vol. 4, 353–385. Published, 06/26/2020.
    https://doi.org/10.1007/s41468-020-00055-x
  • Adam Brown & Bei Wang (2020). Sheaf-theoretic stratification learning from geometric and topo- logical perspectives. Discrete & Computational Geometry. Published, 05/29/2020.
    https://doi.org/10.1007/s00454-020-00206-y
  • Michal Adamaszek, Henry Adams, Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang & Lori Ziegelmeier (2020). On homotopy types of Vietoris-Rips complexes of metric gluings. Journal of Applied and Computational Topology. Published, 05/20/2020.
    https://doi.org/10.1007/s41468-020-00054-y
  • Braxton Osting, Sourabh Palande & Bei Wang (2020). Towards spectral sparsification of simplicial complexes based on generalized effective resistance. Journal of Computational Geometry. Vol. 11. Published, 03/27/2020.
    https://doi.org/10.20382/jocg.v11i1a8
  • Jochen Jankowai, Bei Wang & Ingrid Hotz (2019). Robust extraction and simplification of 2D tensor field topology. Computer Graphics Forum. Vol. 38, 337– 349. Published, 07/10/2019.
    https://doi.org/10.1111/cgf.13693
  • Sourabh Palande, Vipin Jose, Brandon Zielinski, Jeffrey Anderson, P. Thomas Fletcher & Bei Wang (2019). Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference. Brain Connectivity. Vol. 9, 13–21. Published, 02/20/2019.
    https://doi.org/10.1007/978-3-030-32248-9_82
  • Bei Wang, Roxana Bujack, Paul Rosen, Primoz Skraba, Harsh Bhatia, and Hans Hagen. Interpreting Galilean invariant vector field analysis via extended robustness. In Topological Methods in Data Analysis and Visualization V: Theory, Algorithms, and Applications (Proceedings of TopoInVis 2017), accepted. Springer, 2019. Published, 01/2019.
  • Lin Yan, Yaodong Zhao, Paul Rosen, Carlos Scheidegger, and Bei Wang. Homology-preserving dimensionality reduction via manifold landmarking and tearing. Symposium on Visualization in Data Science (VDS) at IEEE VIS, 2018. Published, 10/2018.
  • Keri L. Anderson, Jeffrey S. Anderson, Sourabh Palande, and Bei Wang. Topological data analysis of functional MRI connectivity in time and space domains. In Guorong Wu, Islem Rekik, Markus D. Schirmer, Ai Wern Chung, and Brent Munsell, editors, Connectomics in NeuroImaging (Lecture Notes in Computer Science, Proceedings of International Workshop on Connectomics in NeuroImaging), volume 11083. Springer, 2018. Published, 09/2018.
  • Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang, and Lori Ziegelmeier. A complete characterization of the 1-dimensionalintrinsic Cech persistence diagrams for metric graphs. In Erin Chambers, Brittany Terese Fasy, and Lori Ziegelmeier, editors, Research in Computational Topology, pages 33–56. Springer International Publishing, 2018. Published, 07/2018.
    https://arxiv.org/abs/1702.07379
  • Michal Adamaszek, Henry Adams, Ellen Gasparovic, Maria Gommel, Emilie Purvine, Rad- mila Sazdanovic, Bei Wang, Yusu Wang, and Lori Ziegelmeier. Vietoris-Rips and Cˇech Com- plexes of Metric Gluings. International Symposium on Computational Geometry (SOCG), 2018. Published, 06/2018.
    https://arxiv.org/abs/1712.06224
  • Kevin Knudson and Bei Wang. Discrete Stratified Morse Theory: A User’s Guide. Interna- tional Symposium on Computational Geometry (SOCG), 2018. Published, 06/2018.
    https://arxiv.org/abs/1801.03183
  • Adam Brown and Bei Wang. Sheaf-Theoretic Stratification Learning. International Sympo- sium on Computational Geometry (SOCG), 2018. Published, 06/2018.
    https://arxiv.org/abs/1712.07734
  • Mustafa Hajij, Bei Wang, Carlos Scheidegger, and Paul Rosen. Visual detection of struc- tural changes in time-varying graphs using persistent homology. IEEE Pacific Visualization Symposium (PacificVis), 2018. Published, 04/2018.
    https://arxiv.org/abs/1707.06683
  • Hamish Carr, Michael Kerber, and Bei Wang. Report from Dagstuhl seminar 17292: Topology, computation and data analysis. Dagstuhl Reports, 7(7):88–109, 2018. Published, 01/2018.
  • Brittany Terese Fasy and Bei Wang (with contributions by members of the WinComp- Top community). Open problems in computational topology. SIGACT NEWS Open Problems Column, 48(3), 2017. Published, 11/2017.
  • Tim Sodergren, Jessica Hair, Jeff M. Phillips, and Bei Wang. Visualizing sensor network coverage with location uncertainty. Symposium on Visualization in Data Science (VDS) at IEEE VIS, 2017. Published, 10/2017.
    https://arxiv.org/abs/1710.06925
  • Bei Wang and Ingrid Hotz. Robustness for 2D symmetric tensor field topology. In Thomas Schultz, Evren Ozarslan, and Ingrid Hotz, editors, Modeling, Analysis, and Visualization of Anisotropy, pages 3–27. Springer International Publishing, 2017. Published, 07/2017.
    http://www.sci.utah.edu/~beiwang/publications/Tens...
  • Attila Gyulassy, Aaron Knoll, Kah Chun Lau, Bei Wang, Peer-Timo Bremer, Michael E. Papka, Larry A. Curtiss, and Valerio Pascucci. Morse-Smale analysis of ion diffusion in Ab initio battery materials simulations. In Hamish Carr, Christoph Garth, and Tino Weinkauf, editors, Topological Methods in Data Analysis and Visualization IV: Theory, Algorithms, and Applications (Proceedings of TopoInVis 2015), pages 135–149. Springer, Cham, 2017. Published, 06/2017.
  • Wathsala Widanagamaachchi, Alexander Jacques, Bei Wang, Erik Crosman, Peer-Timo Bremer, Valerio Pascucci, and John Horel. Exploring the Evolution of Pressure-Perturbations to Understand Atmospheric Phenomena. IEEE Pacific Visualization Symposium (PacificVis), 2017. Published, 04/2017.
    https://doi.org/10.1109/PACIFICVIS.2017.8031584
  • Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer, and Valerio Pascucci. Visualizing high-dimensional data: Advances in the past decade. IEEE Transactions on Visualization and Computer Graphics (TVCG), 23(3):1249–1268, 2017. Published, 03/2017.
    https://doi.org/10.1109/TVCG.2016.2640960
  • Hoa Nguyen, Paul Rosen, and Bei Wang. Visual Exploration of Multiway Dependencies in Multivariate Data. ACM SIGGRAPH ASIA Symposium on Visualization, 2016. Published, 12/2016.
    https://doi.org/10.1145/3002151.3002162
  • Eleanor Wong, Sourabh Palande,Bei Wang, Brandon Zielinski, Jeffrey Anderson,and P.Thomas Fletcher. Kernel partial least squares regression for relating functional brain network topology to clinical measures of behavior. IEEE International Symposium on Biomedical Imaging (ISBI), 2015. Published, 06/2016.
    https://doi.org/10.1109/ISBI.2016.7493506
  • Shusen Liu, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Bei Wang, Brian Summa, and Valerio Pascucci. Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data. Computer Graphics Forum (CGF, Proceedings of EuroVis), 35(3):1–10, 2016. Published, 06/2016.
    https://doi.org/10.1111/cgf.12876
  • Primoz Skraba, Paul Rosen, Bei Wang, Guoning Chen, Harsh Bhatia, and Valerio Pascucci. Critical point cancellation in 3D vector fields: Robustness and discussion. IEEE Transactions on Visualization and Computer Graphics (TVCG), 22(6):1683–1693, 2016. Published, 06/2016.
    https://doi.org/10.1109/TVCG.2016.2534538
  • Elizabeth Munch and Bei Wang. Convergence between Categorical Representations of Reeb Space and Mapper. International Symposium on Computational Geometry (SOCG), 2016. Published, 06/2016.
    https://doi.org/10.4230/LIPIcs.SoCG.2016.53
  • Dan Maljovec, Bei Wang, Paul Rosen, Andrea Alfonsi, Giovanni Pastore, Cristian Rabiti, and Valerio Pascucci. Rethinking sensitivity analysis of nuclear simulations with topology. IEEE Pacific Visualization Symposium (PacificVis), pages 64–71, 2016. Published, 04/2016.
    https://doi.org/10.1109/PACIFICVIS.2016.7465252
  • Brittany T. Fasy and Bei Wang. Exploring persistent local homology in topological data anal- ysis. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 6430–6434, 2016. Published, 03/2016.
  • Dan Maljovec, Shusen Liu, Bei Wang, Valerio Pascucci, Peer-Timo Bremer, Diego Mandelli, and Curtis Smith. Analyzing Simulation-Based PRA Data Through Traditional and Topological Clustering: A BWR Station Blackout Case Study. Reliability Engineering & System Safety (RESS), 145:262–276, 2016. Published, 01/2016.
    http://dx.doi.org/10.1016/j.ress.2015.07.001
  • Attila Gyulassy, Aaron Knoll, Kah Chun Lau, Bei Wang, Peer-Timo Bremer, Michael E. Papka, Larry A. Curtiss, and Valerio Pascucci. Interstitial and Interlayer Ion Diffusion Geometry Extraction in Graphitic Nanosphere Battery Materials. IEEE Transactions on Visu- alization and Computer Graphics, 22(1):916–925, 2016. Published, 01/2016.
    https://doi.org/10.1109/TVCG.2015.2467432
  • Elizabeth Munch and Bei Wang. Reeb Space Approximation with Guarantees. Fall Workshop on Computational Geometry (FWCG), 2015. Published, 10/2015.
  • Primoz Skraba, Bei Wang, Guoning Chen, and Paul Rosen. Robustness-Based Simplification of 2D Steady and Unsteady Vector Fields. IEEE Transactions on Visualization and Computer Graphics (TVCG), 21(8):930 – 944, 2015. Published, 08/2015.
  • Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, and Valerio Pascucci. Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Pro- jections. Computer Graphics Forum (CGF, Proceedings of EuroVis), 34(3):271–280, 2015. Published, 07/2015.
  • Jeff M. Phillips, Bei Wang, and Yan Zheng. Geometric inference on kernel density estimates. International Symposium on Computational Geometry (SOCG), pages 857–871, 2015. Published, 06/2015.
  • Harsh Bhatia, Bei Wang, Gregory Norgard, Valerio Pascucci, and Peer-Timo Bremer. Local, Smooth, and Consistent Jacobi Set Simplification. Computational Geometry: Theory and Applications (CGTA), 48(4):311–332, 2015. Published, 05/2015.
  • Peer-Timo Bremer, Dan Maljovec, Avishek Saha, Bei Wang, Jim Gaffney, Brian K. Spears, and Valerio Pascucci. ND2AV: N-Dimensional Data Analysis and Visualization – Analysis for the National Ignition Campaign. Computing and Visualization in Science, 17(1):1–18, 2015. Published, 02/2015.
  • Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, and Valerio Pascucci. Multivariate volume visualization through dynamic projections. IEEE Symposium on Large Data Analysis and Visualization (LDAV), pages 35–42, 2014. Published, 11/2014.
  • Shusen Liu, Bei Wang, Peer-Timo Bremer, and Valerio Pascucci. Distortion-Guided Structure-Driven Interactive Exploration of High-Dimensional Data. Computer Graphics Forum (CGF, Proceedings of EuroVis), 33(3):101–110, 2014. Published, 07/2014.
  • Diego Mandelli, Curtis Smith, Tom Riley, Joseph Nielsen, John Schroeder, Cristian Rabiti, Andrea Alfonsi, Joshua Cogliati, Robert Kinoshita, Valerio Pascucci, Bei Wang, and Dan Maljovec. Overview of new tools to perform safety analysis: BWR station black out test case. In Probabilistic Safety Assessment & Management conference (PSAM), 2014. Published, 06/2014.
  • Dan Maljovec, Bei Wang, John Moeller, and Valerio Pascucci. Topology-based active learning. Technical Report UUSCI-2014-00, University of Utah, 2014. Published, 06/2014.
  • Primoz Skraba, Bei Wang, Guoning Chen, and Paul Rosen. 2D vector field simplification based on robustness. IEEE Pacific Visualization Symposium (PacificVis), pages 49–56, 2014. Published, 04/2014.
  • Primoz Skraba and Bei Wang. Interpreting feature tracking through the lens of robustness. In Peer-Timo Bremer, Ingrid Hotz, Valerio Pascucci, and Ronald Peikert, editors, Topolog- ical Methods in Data Analysis and Visualization III: Theory, Algorithms, and Applications (Proceedings of TopoInVis 2013), pages 19–38. Springer, 2014. Published, 03/2014.
  • Primoz Skraba and Bei Wang. Approximating local homology from samples. ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 174–192, 2014. Published, 01/2014.
  • Dan Maljovec, Bei Wang, Ana Kupresanin, Gardard Johannesson, Valerio Pascucci, and Peer- Timo Bremer. Adaptive sampling with topological scores. International Journal for Uncertainty Quantification (IJUQ), 3(2):119–141, 2013. Published, 11/2013.
  • Dan Maljovec, Bei Wang, Diego Mandelli, Peer-Timo Bremer, and Valerio Pascucci. Analyze dynamic probabilistic risk assessment data through clustering. In International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA), 2013. Published, 09/2013.
  • Bei Wang, Paul Rosen, Primoz Skraba, Harsh Bhatia, and Valerio Pascucci. Visualizing Robustness of Critical Points for 2D Time-Varying Vector Fields. Computer Graphics Forum (CGF, Proceedings of EuroVis), 32(3pt2):221–230, 2013. Published, 07/2013.
  • Dan Maljovec, Bei Wang, Valerio Pascucci, Peer-Timo Bremer, Michael Pernice, Diego Man- delli, and Robert Nourgaliev. Exploration of high-dimensional scalar function for nuclear reactor safety analysis and visualization. International Conference on Mathematics and Com- putational Methods Applied to Nuclear Science & Engineering (M&C), pages 712–723, 2013. Published, 05/2013.
  • Dan Maljovec, Avishek Saha, Peter Lindstrom, Peer-Timo Bremer, Bei Wang, Carlos Correa, and Valerio Pascucci. A comparative study of morse complex approximation using different neighborhood graphs. Topology-Based Methods in Visualization (TopoInVis), 2013. Published, 03/2013.
  • Jeff M. Phillips and Bei Wang. Kernel distance for geometric inference. Fall Workshop on Computational Geometry (FWCG), 2012. Published, 10/2012.
  • A.N.M. Imroz Choudhury, Bei Wang, Paul Rosen, and Valerio Pascucci. Topological analysis and visualization of cyclical behavior in memory reference traces. IEEE Pacific Visualization Symposium (PacificVis), pages 9–16, 2012. Published, 04/2012.
  • Paul Bendich, Bei Wang, and Sayan Mukherjee. Local homology transfer and stratification learning. ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 1355–1370, 2012. Published, 01/2012.
  • Bei Wang, Brian Summa, Valerio Pascucci, and Mikael Vejdemo-Johansson. Branching and Circular Features in High Dimensional Data. IEEE Transactions on Visualization and Computer Graphics (TVCG, Proceedings of SciVis), 17(12):1902–1911, 2011. Published, 11/2011.
  • Bei Wang, Herbert Edelsbrunner, and Dmitriy Morozov. Computing Elevation Maxima by Searching the Gauss Sphere. Journal of Experimental Algorithmics (JEA), 16:1–13, 2011. Published, 01/2011.
  • Paul Bendich, Sayan Mukherjee, and Bei Wang. Towards stratification learning through homology inference. AAAI Fall Symposium on Manifold Learning and its Applications (AAAI), 2010. Published, 10/2010.
  • Mats Enstero, Orjan Akerborg, Daniel Lundin, Bei Wang, Terrence S Furey, Marie Ohman, and Jens Lagergren. A Computational Screen for Site Selective A-to-I Editing Detects Novel Sites in Neuron Specific Hu Proteins. BMC Bioinformatics, 11(6), 2010. Published, 01/2010.
  • Bei Wang, Jeff M. Phillips, Robert Schrieber, Dennis Wilkinson, Nina Mishra, and Robert Tarjan. Spatial scan statistics for graph clustering. SIAM International Conference on Data Mining (SDM), 2008. Published, 04/2008.
  • Bei Wang, Dimitris Papamichail, Steffen Mueller, and Steven Skiena. Two Proteins for the Price of One: The Design of Maximally Compressed Coding Sequences. Natural Computing, 6(4):359–370, 2007. Published, 12/2007.
  • Sudheer Sahu, Bei Wang, and John H. Reif. A framework for modeling DNA based molecular systems. In Chengde Mao and Takashi Yokomori, editors, DNA Computing, Lecture Notes in Computer Science (12th International Meeting on DNA Computing), volume 4287, pages 250–265. Springer, 2006. Published, 06/2006.
  • Tarek M. Sobh, Bei Wang, and Sarosh H. Patel. Web enabled robot design and dynamic control simulation software solutions from task points description. Conference of the IEEE Industrial Electronics Society (IECON), 2:1221–1227, 2003. Published, 11/2003.
  • Tarek M. Sobh, Bei Wang, and Kurt W. Coble. Experimental Robot Musicians. Journal of Intelligent and Robotic Systems (JIRS), 38(2):197–212, 2003. Published, 01/2003.

Research Statement

My research expertise lies in the theoretical, algorithmic, and application aspects of data analysis and data visualization, with a focus on topological techniques.
 
My vision is to tackle problems involving large and complex forms of data that typically require rich (often topological) structural descriptions when geometric and statistical intuitions alone are not sufficient. My research leverages topological data analysis, which provides a strong theoretical basis for transforming large, complex data into compact, structure-highlighting representations. Topological data analysis captures complex interactions in a system via simplicial complexes, describes features at all scales via persistent homology, is robust with respect to noise, and provides efficient computation. Such a versatile approach connects naturally with and provides infrastructures for data visualization, and spurs the rethinking of interactive data exploration to facilitate analytical reasoning.
 
My training, experience, and scientific approach have put me in a unique position to be both a data theorist and data practitioner. My training in computational topology lies at the intersection of computer science and mathematics. My doctoral dissertation focused on theoretical and algorithmic approaches to infer statistical, geometric and topological structures from graphs and high-dimensional point cloud data. My postdoctoral experience has added an extra dimension to my research portfolio in employing theoretical work from topological data analysis for the visualization of complex data. I have also worked on interdisciplinary research projects ranging from computational biology and bioinformatics to nuclear sensitivity analysis. 
 
In the research methodology I employ, innovation and progress in data science require an end-to-end view. Theoretical advancements need to be transformed into robust and efficient algorithms, which are then applied to and validated by real-world data. Meanwhile, large and complex data constantly motivate and inspire new theoretical and algorithmic developments. As a consequence, I publish my work in premier venues from theoretical computer science to visualization, as well as in application domains.
 
Complex data that arise from my existing and emerging research include point cloud data from manifolds and non-manifolds, stationary and time-varying vector fields, high-dimensional parameter spaces, volumetric data, large-scale networks, and multivariate ensembles. My contributions help to reimagine the complex data spaces by enriching them with formal structures via topological techniques that completely transform how we should conceptualize and work with data.

Research Keywords

  • Topological Data Analysis
  • Scientific Visualization
  • Machine Learning
  • Information Visualization
  • Data Visualization
  • Data Mining
  • Data Analysis
  • Computational Topology
  • Computational Geometry
  • Computational Biology
  • Applied Topology

Presentations

  • Machine Learning on Higher-Order Structured data (ML-HOS) Workshop at ICDM 2022. Invited Talk/Keynote, Presented, 11/28/2023.
  • AI Seminar at ScaDS.AI (Center for Scalable Data Analytics and Artificial Intelligence), Leipzig University. Invited Talk/Keynote, Presented, 05/15/2023.
  • Colorado State University Topology Seminar. Invited Talk/Keynote, Presented, 04/18/2023.
  • Northeastern Topology Seminar, Northeastern University . Invited Talk/Keynote, Presented, 04/11/2023.
  • Dagstuhl Seminar on Set Visualization and Uncertainty, Germany. Visualizing Hypergraphs With Connections to Uncertainty Visualization. Invited Talk/Keynote, Presented, 11/14/2022.
  • Stochastic Seminar, Department of Mathematics, University of Utah . Invited Talk/Keynote, Presented, 11/04/2022.
  • Mini Symposium on Statistics and Machine Learning in Topological and Geometric Data Analysis at SIAM Conference on Mathematics of Data Science (MDS22). Invited Talk/Keynote, Presented, 09/29/2022.
  • Department of Energy Computer Graphics Forum . Invited Talk/Keynote, Presented, 08/30/2022.
  • Utah Center for Data Science (UCDS) Data Science Seminar. Invited Talk/Keynote, Presented, 08/24/2022.
  • Applied Topology in Frontier Sciences. Applied, Combinatorial and Toric Topology. Institute for Mathematical Sciences, Singapore. Invited Talk/Keynote, Presented, 07/18/2022.
  • Spring Western AMS Sectional Meeting, special session on Computational Topology and Applications. Invited Talk/Keynote, Presented, 05/14/2022.
  • University of Iowa Mathematical Biology Seminar, April 18, 2022. Invited Talk/Keynote, Presented, 04/18/2022.
  • Pacific Northwest National Laboratory (PNNL) Mathematics for Artificial Reasoning in Science (MARS) Seminar Series. Invited Talk/Keynote, Presented, 01/2021.
  • Joint Mathematics Meetings (JMM) AMS Special Session on Combinatorial Approaches to Topological Structures. Invited Talk/Keynote, Presented, 01/2021.
  • Applied Topology Seminar at Swiss Federal Institute of Technology Lausanne (EPFL). Invited Talk/Keynote, Presented, 11/2020.
  • Machine Learning Seminar at Florida State University. Invited Talk/Keynote, Presented, 10/2020.
  • High-Performance Computing (HPC) China Seminar. Invited Talk/Keynote, Presented, 09/2020.
  • MBI Optimal Transport Workshop: Optimal Transport, Topological Data Analysis and Ap- plications to Shape and Machine Learning. Invited Talk/Keynote, Presented, 07/2020.
  • Applied Algebraic Topology Research Network. Invited Talk/Keynote, Presented, 05/2020.
  • NII Shonan Meeting on Analyzing Large Collections of Time Series. Invited Talk/Keynote, Presented, 02/2018.
  • Discrete Math Seminar Talk, University of South Florida. Invited Talk/Keynote, Presented, 10/2017.
  • Interdisciplinary Data Science Consortium, University of South Florida. Invited Talk/Keynote, Presented, 10/2017.
  • Topology Seminar Talk, University of Florida. Invited Talk/Keynote, Presented, 10/2017.
  • Math Department Colloquium, University of South Florida. Invited Talk/Keynote, Presented, 10/2017.
  • BIRS Workshop: Topological Data Analysis: Developing Abstract Foundations. Other, Presented, 07/2017.
  • BIRS Workshop: Topological Methods in Brain Network Analysis. Other, Presented, 05/2017.
  • Dagstuhl Seminar: Computational Geometry. Other, Presented, 04/2017.
  • Topology-Based Methods in Visualization (TopoInVis), 2017. Conference Paper, Refereed, Presented, 02/2017.
    http://fj.ics.keio.ac.jp/topoinvis/
  • Topological Data Analysis and Related Topics (TDART), AIMR Tohoku University Advanced Institute for Materials Research, 2017. Invited Talk/Keynote, Presented, 02/2017.
    http://www.wpi-aimr.tohoku.ac.jp/hiraoka_labo/Conf...
  • International Workshop on Topological Data Analysis in Biomedicine (TDA-Bio) Seattle, WA, October 2, 2016. Part of the 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB). Other, Presented, 10/02/2016.
    http://www.sci.utah.edu/~beiwang/acmbcbworkshop201...
  • Invited Panel Speaker, Future in Review (FiRe) Conference, 2016. Invited Talk/Keynote, Presented, 10/2016.
    https://www.futureinreview.com/
  • IEEE VIS Tutorial: Recent Advancement in Feature-based Flow Visualization, 2016. Other, Presented, 10/2016.
    https://ieeevis.org/
  • HEAP Seminar: High Energy and Astrophysics Seminar Series, Department of Physics & Astronomy, University of Utah, 2016. Invited Talk/Keynote, Presented, 09/16/2016.
    http://www.physics.utah.edu/~heap/
  • Poster Presentation: Workshop for Women in Computational Topology (WinCompTop), August 15 - 19, 2016. Other, Presented, 08/2016.
    https://www.ima.umn.edu/2015-2016/SW8.15-19.16
  • International Symposium on Computational Geometry, 2016. Conference Paper, Refereed, Presented, 06/2016.
    http://socg2016.cs.tufts.edu/
  • Presentation: at Visualization Summer Camp, June 17-20, 2016. Other, Presented, 06/2016.
    http://valt.cs.tufts.edu/vissummercamp/
  • Excellence Center at Linkooping - Lund on Information Technology (ELLIIT) distinguished lecture, Linkooping University, Sweden, 2016. Invited Talk/Keynote, Presented, 05/2016.
    https://www.liu.se/elliit
  • Topology, Geometry, and Data Analysis Conference at Ohio State University, 2016. Invited Talk/Keynote, Presented, 05/2016.
    http://www.tgda.osu.edu/tgda-conf-osu.html
  • IEEE Pacific Visualization (PacificVis), 2016. Conference Paper, Refereed, Presented, 04/2016.
    http://www.pvis.org

Languages

  • English, fluent.
  • Mandarin Chinese, fluent.

Software Titles

  • TopoAct: Visually Exploring the Shape of Activations in Deep Learning. TopoAct is a visual exploration system used to study topological summaries of activation vectors for deep neural networks. We present visual exploration scenarios using TopoAct that provide valuable insights towards learned representations of image classifiers such as GoogLeNet and ResNet. Release Date: 12/09/2020. Distribution List: https://github.com/tdavislab/TopoAct.
  • Metaboverse: Automated Exploration and Contextualization of Metabolic Data. Metaboverse is an interactive visualization tool for automated exploration and contextualization of metabolic data. Integrating multi-omic or single-omic metabolic data upon the metabolic network can be challenging for a variety of reasons. Metaboverse seeks to simplify this task for users by providing a simple, user-friendly interface for layering their data on a dynamic representation of the metabolic network. Additionally, it provides several new tools to enable the contextualization of metabolic data. Release Date: 06/28/2020. Distribution List: https://github.com/Metaboverse/.
  • Mapper Interactive. Mapper Interactive provides a library of easily-extendable modules for developing interactive visualization of high-dimensional data using the mapper construction. It is fairly lightweight, and helps to rapidly explore the parameter space of the mapper construction interactively. Release Date: 05/18/2020. Distribution List: https://mapperinteractive.github.io/.
  • MVF Designer: Design and Visualization of Morse Vector Fields. MVF Designer is an interactive tool that enables the design and analysis of 2D Morse vector fields via elementary moves. Release Date: 02/25/2020. Distribution List: https://github.com/zhou325/VIS-MSVF.