Bei Wang

Bei Wang Phillips
TDAVIS Lab
Topological Data Analysis and Data Visualization

Click here for high-res photo

Bei Wang Phillips
Publish Under: Bei Wang

Associate Professor, Kahlert School of Computing
Adjunct Associate Professor, Department of Mathematics
Faculty member, Scientific Computing and Imaging (SCI) Institute
University of Utah

Warnock Engineering Building (WEB) Room 4608
72 South Central Campus Drive
Salt Lake City, Utah 84112

Email: beiwang AT sci.utah.edu

Research keywords: topological data analysis, data visualization, computational topology, data mining, and machine learning.

CV:

(last update February 17, 2024)

Web page last update: January 19, 2025.

Lab News

Dr. Bei Wang Phillips was awarded the Presidential Early Career Award for Scientists and Engineers (PECASE) Award by President Biden, the highest honor bestowed by the U.S. government on outstanding scientists and engineers early in their careers. January, 2025.
Dr. Lin Yan won the IEEE 2024 VGTC Visualization Dissertation Award, which recognizes outstanding academic research and development in visualization and visual analytics. October, 2024.
Dr. Youjia Zhou won the Price College of Engineering Outstanding Dissertation for 2023 at the University of Utah. May, 2024.

Short Biography

Dr. Bei Wang Phillips is an Associate Professor in the School of Computing and a faculty member in the Scientific Computing and Imaging (SCI) Institute, University of Utah. She obtained her Ph.D. in Computer Science from Duke University. Her research focuses on topological data analysis, data visualization, and computational topology. She works on combining topological, geometric, statistical, data mining, and machine learning techniques with visualization to study large and complex data for information exploration and scientific discovery. Some of her current research activities involve the analysis and visualization of high-dimensional point clouds, scalar fields, vector fields, tensor fields, networks, and multivariate ensembles. Her research has been supported by multiple awards from NSF, NIH, and DOE. Dr. Phillips was a DOE Early Career Research Program (ECRP) awardee in 2020 and an NSF CAREER awardee in 2022. She was awarded the Presidential Early Career Award for Scientists and Engineers (PECASE) Award in 2025.

Professional Experience

Associate Professor, School of Computing, University of Utah (2022 - present)
Faculty Member, Scientific Computing and Imaging (SCI) Institute , University of Utah (2016 - Present)
Adjunct Associate Professor, Department of Mathematics, University of Utah (2022 - present)
Assistant Professor, School of Computing, University of Utah (2016 - 2022)
Adjunct Assistant Professor, Department of Mathematics, University of Utah (2019 - 2022)
Research Computer Scientist, Scientific Computing and Imaging Institute (SCI), University of Utah (2011 - 2016)
Postdoctoral Fellow, Scientific Computing and Imaging Institute (SCI), University of Utah (2010 - 2011)
Visiting Researcher, Institute of Science and Technology Austria (IST Austria) (Fall 2009)

Education

Ph.D. in Computer Science, Duke University (2010), Advisor: Herbert Edelsbrunner
Certificate in Computational Biology and Bioinformatics, Duke University (2010)
B.S. in Computer Science and Mathematics, Minor in Psychology, Summa Cum Laude, University of Bridgeport (2003)

Best Paper Awards

Honorable Mention Paper Award at IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis), 2023.
Visual Computer Cover of the Year 2023, by Daniel Klötzl, Tim Krake, Youjia Zhou, Ingrid Hotz, Bei Wang, Daniel Weiskopf, 2023.
Visual Computer Second Best Paper Award at Computer Graphics International (CGI), 2022.
Honorable Mention Paper Award at IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis), 2022.
Best Paper at Shape Modeling International (SMI), 2019.
Best Poster at China Visualization and Visual Analytics Conference (ChinaVis), 2019.
Best Paper at International Workshop on Connectomics in NeuroImaging (CNI) at MICCAI, 2018.
Best Paper at IEEE Pacific Visualization (PacificVis), 2016.
Best Paper at IEEE Pacific Visualization (PacificVis), 2014.
First runner-up for the Best Student Paper Award at International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA), 2013. Student: Dan Maljovec.

Student Awards

Lin Yan: IEEE VGTC Visualization Dissertation Award, 2024.
Youjia Zhou: Price College of Engineering Outstanding Dissertation for 2023, University of Utah, 2024.
Nithin Chalapathi: Finalist in the Computing Research Association (CRA) Outstanding Undergraduate Researcher Awards, 2021.
Adam Brown: ISTplus Postdoctoral Fellowship Awardee from IST Austria, 2019-2021.
Yiliang Shi: Honorable Mention in the Computing Research Association (CRA) Outstanding Undergraduate Researcher Awards, 2018.
Keri Anderson: School of Computing Outstanding Graduating Senior award at the University of Utah, 2018.
William Garnes: College of Engineering Scholarship, 2017-2018. GEM fellowship from the National GEM Consortium, 2018.

Research Interests

I am interested in the analysis and visualization of large and complex data.

My research expertise lies in the theoretical, algorithmic, and application aspects of data analysis and data visualization, with a focus on topological techniques. My research interests include: topological data analysis, data visualization, computational topology, computational geometry, machine learning and data mining. Previously, I have worked on projects related to computational biology and bioinformatics, as well as robotics.

My vision is to tackle problems involving large and complex forms of data that require rich structural descriptions, by combining topological, geometric, statistical, data analysis and visualization techniques.

Current Projects

	2023 - 2026 NSF DMS-2301361 Multiparameter Topological Data Analysis. Project Website
	2023 - 2026 NSF OAC-2313124 Topology-Aware Data Compression for Scientific Analysis and Visualization. Project Website
	2022 - 2027 NSF IIS-2145499 CAREER: A Measure Theoretic Framework for Topology-Based Visualization. Project Website.
	2022 - 2026 NSF IIS-2205418 SCH: Geometry and Topology for Interpretable and Reliable Deep Learning in Medical Imaging Project Website.
	2022 - 2025 DOE DE-SC0023157 Implicit Continuous Representations for Visualization of Complex Data. Utah Project Website.
	2021 - 2024 NSF DMS-2134223 Advancing Theoretical Minimax Deep Learning: Optimization, Resilience, and Interpretability. Project Website.
	2020 - 2025 DOE DE-SC0021015 Topology-Preserving Data Sketching for Scientific Visualization. Project Website.
	2021 - 2024 Utah Board of Higher Education Deep Technology Initiative Bringing Fairness in AI to the Forefront of Education. Project Website.

Past Projects

	2019 - 2022 NSF IIS-1910733 Visualizing Robust Features in Vector and Tensor Fields. Project Website.
	2017 - 2020 NSF DBI-1661375 ABI Innovation: A Scalable Framework for Visual Exploration and Hypotheses Extraction of Phenomics Data using Topological Analytics. Utah Project Website. Collaborative Project Website.
	2015 - 2019 NSF IIS-1513616 Topological Data Analysis for Large Network Visualization. Project Web Site.
	2016 - 2019 NIH 1R01EB022876 Beyond Diagnostic Classification of Autism. Project Website.
	2016 - 2017 NRAO-NSF Pilot Grant Feature Extraction & Visualization of ALMA Data Cubes through Topological Data Analysis. Project Web Site.

Organized Workshops/Tutorials

2023: A Hands-on TTK Tutorial for Absolute Beginners at IEEE VIS Conference, October 22, 2023.
Organizers: Bei Wang, Christoph Garth, Robin Maack, Mathieu Pont, Julien Tierny, Florian Wetzels, Michael Will.

2023: Dagstuhl Seminar: Topological Data Analysis and Applications, May 7-12, 2023.
Organizers: Bei Wang, Ulrich Bauer, Vijay Natarajan.

2022: Topological Analysis of Ensemble Scalar Data with TTK, A Sequel at IEEE VIS Conference, October 16-21, 2022.
Organizers: Bei Wang, Christoph Garth, Charles Gueunet, Pierre Guillou, Federico Iuricich, Joshua A Levine, Jonas Lukasczyk, Mathieu Pont, Julien Tierny, Jules Vidal, Florian Wetzels.

2022: AWM Research Symposium Special Session on Topological Data Analysis, June 16-19, 2022.
Organizers: Bei Wang, Radmila Sazdanovic, Lori Ziegelmeier.

2022: Topological Data Visualization Workshop, May 16-20, 2022.
Organizers: Bei Wang, Isabel Darcy.

2021: Topological Analysis of Ensemble Scalar Data with TTK at IEEE VIS Conference, October 24-29, 2021.
Organizers: Bei Wang, Christoph Garth, Charles Gueunet, Pierre Guillou, Lutz Hofmann, Joshua A Levine, Jonas Lukasczyk, Julien Tierny, Jules Vidal, Florian Wetzels.

2021: A Visual Tour of Bias Mitigation Techniques for Word Representations at KDD Tutorial, August 14-18, 2021.
Organizers: Bei Wang, Sunipa Dev, Jeff Phillips, Archit Rathore, Vivek Srikumar.

2021: Geometric and Topological Methods in Biomedical Image Analysis during the Computational Geometry Week, June 7-11, 2021.
Organizers: Bei Wang, Chao Chen.

2021: A Visual Tour of Bias Mitigation Techniques for Word Representations at AAAI Tutorial Forum, February 3, 2021.
Organizers: Bei Wang, Sunipa Dev, Jeff Phillips, Archit Rathore, Vivek Srikumar.

2020: Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, Educating the Earth at IEEE VIS, October 25-30, 2020.
Organizers: Bei Wang, Juna Kollmeier, Lauren Anderson.

2020: Application Spotlights: Challenges in the Visualization of Bioelectric Fields for Cardiac and Neural Research at IEEE VIS, October 25-30, 2020.
Organizers: Bei Wang, Rob MacLeod, Wilson Good.

2020: Visualization in Astrophysics: Carnegie + SCI Mini-Workshop at SCI, April 27, 2020.
Organizers: Bei Wang, Juna Kollmeier, Lauren Anderson.

2019: 8th Annual Minisymposium on Computational Topology during the Computational Geometry Week, June 18-21, 2019.
Organizers: Bei Wang, Bala Krishnamoorthy, Dmitriy Morozov.

2019: Dagstuhl Seminar: Topology, Computation and Data Analysis, May 19 - 24, 2019.
Organizers: Bei Wang, Michael Kerber, Vijay Natarajan.

2017: Dagstuhl Seminar: Topology, Computation and Data Analysis, July 16 - 21, 2017.
Organizers: Bei Wang, Hamish Carr, Michael Kerber.

2017: 6th Annual Minisymposium on Computational Topology during the Computational Geometry Week, July 4-7, 2017.
Organizers: Bei Wang, Mickael Buchet, Emerson G. Escolar, Clement Maria.

2016: Tutorial: Recent Advancements of Feature-based Flow Visualization and Analysis at IEEE VIS, 2017.
Organizers: Bei Wang, Jun Tao, Hanqi Guo, Tino Weinkauf, Christoph Garth.

2016: Topological Data Analysis in Biomedicine at the 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), Oct 2, 2016.
Organizers: Bei Wang, Bala Krishnamoorthy.

Students

I am looking for talented undergraduate and graduate students who are interested in data analysis and data visualization.
If you are interested and you are a University of Utah student, please email me your CV.
If you have previous research experiences in computational topology, topological data analysis, data visualization, and computational geometry, please email me your CV.
If you have previously published in machine learning and data mining, please email me your CV.
If you are applying to graduate school at School of Computing, University of Utah, please apply through the official admission webpage at http://www.cs.utah.edu/graduate/admissions/.

Current Students

PhD

Mingzhe Li (CS PhD, Fall 2020 - present, expected graduation: Spring 2025)
Xinyuan Yan (CS PhD, Fall 2021 - present, expected graduation: Spring 2026)
Weiran (Nancy) Lyu (CS PhD, Fall 2021 - present, expected graduation: Spring 2026)
Guanqun Ma (CS PhD, Fall 2022 - present, expected graduation: Spring 2027)
Nathaniel Gorski (CS PhD, Fall 2023 - present, expected graduation: Spring 2028)
Dhruv Meduri (CS PhD, Fall 2023 - present, expected graduation: Spring 2028)
Tushar Jain (CS PhD, Fall 2024 - present, expected graduation: Spring 2029)
Colin Denis (Math PhD, Fall 2024 - present, rotation)

Former Postdocs/Students

Postdocs

Raghavendra Sridharamurthy (Postdoctoral Fellow, Spring 2023 - Spring 2024), Assistant Professor @ International Institute of Information Technology Hyderabad (IIIT Hyderabad).
Ding Han (Visiting Scholar, Spring 2022 - Fall 2023), Professor @ Institute of Robotics Robotics, Shanghai Jiao Tong University, China.
Salman Parsa (Postdoctoral Fellow, Spring 2022 - Summer 2022), Assistant Professor @ DePaul University.

PhD

Zhichao Xu (CS PhD, Fall 2020 - Fall 2024). Co-advised with Vivek Srikumar. Applied Scientist @ Amazon Web Services, USA.
Fangfei (Fei) Lan (CS PhD, Fall 2019 - Fall 2024), PostDoc @ University of Lausanne, Switzerland.
Youjia Zhou (CS PhD, Fall 2018 - Spring 2023), first job @ Lucid Software, now Research Scientist @ Meta.
Archit Rathore (CS PhD, Fall 2017 - Spring 2022), first job Machine Learning Engineer @ Stripe, Inc.
Lin Yan (CS PhD, Fall 2017 - Spring 2022), PostDoc @ Argonne National Laboratory, now Assistant Professor @ Iowa State University.
Sourabh Palande (CS PhD, Fall 2015 - Summer 2020), PostDoc @ Michigan State University, now Data Scientist II @ Donald Danforth Plant Science Center.
Adam Brown (Math PhD, Spring 2017 - Spring 2019), informal advising, PostDoc @ IST Austria then @ University of Oxford, now @ Susquehanna International Group.

Gabrielius (Gabe) Aleksandras Kudirka (CS MS RA, Fall 2022 - Spring 2023), first job @ Ford.
Ilkin Safarli (CS MS RA, Spring 2020 - Spring 2021), first job @ Google.
Michael Young (CS MS RA, Spring 2020 - Spring 2021), now @ Built For Teams.
Yulong Liang (CS MS Thesis, Spring 2018 - Spring 2019), first job @ Microsoft.
Yaodong Zhao (CS MS RA, Fall 2017 - Spring 2019), first job @ LeanTaaS.
Avani Sharma (CS MS Thesis, Fall 2016 - Spring 2018), first job @ Goldman Sachs.
Yixuan (Eric) Wang (ECE/CS MS Project, Fall 2016 - Spring 2017), first Job @ InsideSales, now @ Amazon.

Undergraduates

Carson Storm (CS Undergraduate RA, Spring 2023), graduate school @ University of Utah Math
Austin Yang Li (CS Undergraduate RA, Fall 2022 - Spring 2023, expected graduation Spring 2025)
Yi (Ama) Gan (CS Undergraduate RA, Fall 2022 - Spring 2023, graduated Spring 2024)
Nithin Chalapathi (CS Undergraduate Thesis, Spring 2019 - Spring 2021), graduate school @ UC Berkeley.
Yiliang Shi (CS Undergraduate Thesis, Fall 2017 - Spring 2018), graduate school @ Columbia University.
Keri Anderson (CS Undergraduate Thesis, Fall 2017 - Spring 2018).
William Garnes (CS Undergraduate REU, Fall 2017 - Spring 2018), graduate school @ Clemson University.

Research Group Alumni (Rotation, Independent Study, Project Mentoring)

Syed Fahim Ahmed (CS PhD): RA, Spring 2023 - Fall 2024. Milena Belianovich (CS PhD): Rotation, Fall 2022. Xiaoya Tang (CS PhD): Rotation, Fall 2022. Khawar Murad Ahmed (CS PhD): Rotation, Spring 2022. Tripti Agarwal (CS PhD): Rotation, Spring 2021. David Miller (CS PhD): Rotation, Fall 2020. Paul Kristoffersen (CS PhD): Rotation, Spring 2019. Sravan Neerati (CS MS): RA, Fall 2017. Chetal Patil (CS MS): RA, Fall 2017. Tim Sodergren (CS PhD): RA, Fall 2016 - Summer 2017. Vipin Jose (CS MS): RA, Spring 2017. Adam Conkey (CS PhD): Rotation, Spring 2017. Matt Howa (Undergraduate REU): Spring 2017. Sam Leventhal (CS PhD): Independent Study, Spring 2016. Soumya S. Mishra (CS MS): Independent Study, Fall 2014. Project mentoring as a Research Computer Scientist (2011-2016): Brian Summa (CS PhD), Harsh Bhatia (CS PhD), Yan Zheng (CS PhD), Hoa Nguyen (CS PhD), Wathsala Widanagamaachchi (CS PhD), Dan Maljovec (CS PhD), Shusen Liu (CS PhD), Liang He (CS MS).

Teaching

Current Teaching

Spring 2025: CS 6966 - Advanced Data Visualization

Past Teaching

Fall 2024: CS 3090 - Ethics in Computing

Fall 2023: On teaching release (sabbatical)

Spring 2024: On teaching release (sabbatical)

Spring 2023: CS 3960 - Algorithm Fairness in Machine Learning

Fall 2022: CS 2100 - Discrete Structures

Spring 2022: COMP 5360 / MATH 4100 - Introduction to Data Science

Fall 2021: CS 6965 - Advanced Data Visualization

Spring 2021: CS 6170 - Computational Topology

Fall 2020: CS 2100 - Discrete Structures

Spring 2020: CS 2100 - Discrete Structures

Fall 2019: CS 6965 - Advanced Data Visualization

Spring 2019: CS 6170 - Computational Topology

Fall 2018: On teaching release. Visiting Simons Institute at UC Berkeley: Foundations of Data Science program.

Spring 2018: CS 6965 - Advanced Data Visualization. Award: College of Engineering Top Instructor, Spring 2018.

Fall 2017: CS 2100 - Discrete Structures

Fall 2017: CS 7941 - Data Group Seminar

Spring 2017: CS 6170 - Computational Topology

Fall 2016: CS 6210 - Advanced Scientific Computing I

Fall 2016: CS 7941 - Advanced Data Seminar

Spring 2016: CS 1060 - Explorations in Computer Science

Spring 2016: CS 4960 - Introduction to Computational Geometry

Fall 2015: CS 6210 - Advanced Scientific Computing I

Youtube Channels

Utah SoC Computational Topology: Spring 2021
Utah SoC Discrete Structures: Fall 2020 and Spring 2021
Utah SoC Advanced Data Visualization: Fall 2021

Recent Manuscripts

Some but not all manuscripts are on arXiv.

	Geometry of the Space of Partitioned Networks: A Unified Theoretical and Computational Framework. Stephen Y Zhang, Fangfei Lan, Youjia Zhou, Agnese Barbensi, Michael P H Stumpf, Bei Wang, Tom Needham. Manuscript, 2024. arXiv:2409.06302
	NEOviz: Uncertainty-Driven Visual Analysis of Asteroid Trajectories. Fangfei Lan, Malin Ejdbo, Joachim Moeyens, Bei Wang, Anders Ynnerman, Alexander Bock. Manuscript, 2024. arXiv:2411.02812
	A Survey of Simplicial, Relative, and Chain Complex Homology Theories for Hypergraphs. Ellen Gasparovic, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang, Lori Ziegelmeier. Manuscript, 2024. arXiv:2409.18310
	Bounding the Interleaving Distance for Mapper Graphs with a Loss Function. Erin W. Chambers, Elizabeth Munch, Sarah Percival, Bei Wang. Manuscript, 2023. arXiv:2307.15130
	Flexible and Probabilistic Topology Tracking with Partial Optimal Transport. Mingzhe Li, Xinyuan Yan, Lin Yan, Tom Needham, Bei Wang. Manuscript, 2023. arXiv:2302.02895.
	The SVD of Convolutional Weights: A CNN Interpretability Framework. Brenda Praggastis, Davis Brown, Carlos Ortiz Marrero, Emilie Purvine, Madelyn Shapiro, Bei Wang. Manuscript, 2022. arXiv:2208.06894.

Recent Publications

2025
	Tracking the Persistence of Harmonic Chains: Barcode and Stability. Tao Hou, Salman Parsa, Bei Wang. International Symposium on Computational Geometry (SOCG), accepted, 2025. arXiv:2412.15419.
	A General Framework for Augmenting Lossy Compressors with Topological Guarantees. Nathaniel Gorski, Xin Liang, Hanqi Guo, Lin Yan, and Bei Wang. IEEE Pacific Visualization Symposium (PacificVis) TVCG Journal Track, accepted, 2025. IEEE Transactions on Visualization and Computer Graphics, accepted, 2025.
2024
	Intrinsic Interleaving Distance for Merge Trees. Ellen Gasparovic, Elizabeth Munch, Steve Oudot, Katharine Turner, Bei Wang, Yusu Wang. La Matematica, 2024. Download online. DOI: 10.1007/s44007-024-00143-9 arXiv:1908.00063.
	Labeled Interleaving Distance for Reeb Graphs. Fangfei Lan, Salman Parsa, Bei Wang. Journal of Applied and Computational Topology, 8, pages 2367-2399, 2024. DOI:10.1007/s41468-024-00193-6 arXiv:2306.01186
	Distributed Augmentation, Hypersweeps, and Branch Decomposition of Contour Trees for Scientific Exploration. Mingzhe Li, Hamish Carr, Oliver Rubel, Bei Wang, Gunther H. Weber. IEEE Visualization Conference (IEEE VIS), 2024. IEEE Transactions on Visualization and Computer Graphics, 2024. DOI:10.1109/TVCG.2024.3456322 arXiv:2408.04836
	Fast Comparative Analysis of Merge Trees Using Locality-Sensitive Hashing. Weiran Lyu, Raghavendra Sridharamurthy, Jeff M. Phillips, Bei Wang. IEEE Visualization Conference (IEEE VIS), 2024. IEEE Transactions on Visualization and Computer Graphics, 2024. DOI:10.1109/TVCG.2024.3456383 arXiv:2409.08519
	MSz: An Efficient Parallel Algorithm for Correcting Morse-Smale Segmentations in Error-Bounded Lossy Compressors. Yuxiao Li, Xin Liang, Bei Wang, Yongfeng Qiu, Lin Yan, Hanqi Guo. IEEE Visualization Conference (IEEE VIS), 2024. IEEE Transactions on Visualization and Computer Graphics, 2024. DOI:10.1109/TVCG.2024.3456337 Supplement 1. Supplement 2: Detailed figures from Figure 7 and Figure 8 in the main paper. arXiv:2406.09423
	Critical Point Extraction from Multivariate Functional Approximation. Guanqun Ma, David Lenz, Tom Peterka, Hanqi Guo, Bei Wang. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS, 2024. arXiv:2408.13193
	ChannelExplorer: Visual Analytics at Activation Channel's Granularity (Poster). Rahat Zaman, Bei Wang, Paul Rosen. IEEE Visualization Conference (IEEE VIS) Posters, 2024.
	EulerMerge: Simplifying Euler Diagrams Through Set Merges. Xinyuan Yan, Peter Rodgers, Peter Rottmann, Daniel Archambault, Jan-Henrik Haunert, Bei Wang. Proceedings of the 14th International Conference on the Theory and Application of Diagrams (DIAGRAMS), 2024. DOI:10.1007/978-3-031-71291-3_16
	PersiSort: A New Perspective on Adaptive Sorting Based on Persistence. Jens Kristian Refsgaard Schou, Bei Wang. Proceedings of the 36th Canadian Conference on Computational Geometry (CCCG), 2024. Proceedings online.
	Position: Topological Deep Learning is the New Frontier for Relational Learning. Theodore Papamarkou, Tolga Birdal, Michael Bronstein, Gunnar Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi. Proceedings of the 41st International Conference on Machine Learning (ICML), 2024. arXiv:2402.08871
	Interpreting and generalizing deep learning in physics-based problems with functional linear models. Amirhossein Arzani, Lingxiao Yuan, Pania Newell, Bei Wang. Engineering with Computers, 2024. DOI:10.1007/s00366-024-01987-z arXiv:2307.04569.
	Topological Characterization and Uncertainty Visualization of Atmospheric Rivers. Fangfei Lan, Brandi Gamelin, Lin Yan, Jiali Wang, Bei Wang, Hanqi Guo. Eurographics Conference on Visualization (EuroVis), 2024. Computer Graphics Forum, 43(3), e15084, 2024. DOI:10.1111/cgf.15084
	Generating Euler Diagrams Through Combinatorial Optimization. Peter Rottmann, Peter Rodgers, Xinyuan Yan, Daniel Archambault, Bei Wang, Jan-Henrik Haunert. Eurographics Conference on Visualization (EuroVis), 2024. Computer Graphics Forum, 43(3), e15089, 2024. DOI:10.1111/cgf.15089
	In-Context Example Ordering Guided by Label Distributions. Zhichao Xu, Daniel Cohen, Bei Wang, Vivek Srikumar. Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2024. arXiv:2402.11447.
	Labeled Interleaving Distance for Reeb Graphs (Abstract). Fangfei Lan, Salman Parsa, Bei Wang. International Symposium on Computational Geometry (SOCG) Young Researcher Forum (YRF), 2024.
	Computing Loss Function to Bound the Interleaving Distance for Mapper Graphs (Abstract). Erin Wolf Chambers, Ishika Ghosh, Elizabeth Munch, Sarah Percival, Bei Wang. International Symposium on Computational Geometry (SOCG) Young Researcher Forum (YRF), 2024.
	Measure-Theoretic Reeb Graphs and Reeb Spaces. Qingsong Wang, Guanqun Ma, Raghavendra Sridharamurthy, Bei Wang. International Symposium on Computational Geometry (SOCG), 2024. DOI:10.4230/LIPIcs.SoCG.2024.80 arXiv:2401.06748.
	Interactive Visualization of Time-Varying Flow Fields Using Particle Tracing Neural Networks. Mengjiao Han, Sudhanshu Sane, Jixian Li, Shubham Gupta, Bei Wang, Steve Petruzza, Chris R. Johnson. IEEE Pacific Visualization Symposium (PacificVis), 2024. Supplementary Material.
	Exploring Visualization for Fairness in AI Education. Xinyuan Yan, Youjia Zhou, Arul Mishra, Himanshu Mishra, Bei Wang. IEEE Pacific Visualization Symposium (PacificVis), 2024. Supplementary Material.
2023
	TopoSZ: Preserving Topology in Error-Bounded Lossy Compression. Lin Yan, Xin Liang, Hanqi Guo, Bei Wang. IEEE Visualization Conference (IEEE VIS), 2023. IEEE Transactions on Visualization and Computer Graphics (TVCG), 30, pages 1302-1312, 2024. Supplementary Material. DOI:10.1109/TVCG.2023.3326920 arXiv:2304.11768.
	TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclone. Lin Yan, Hanqi Guo, Tom Peterka, Bei Wang, Jiali Wang. IEEE Visualization Conference (IEEE VIS), 2023. IEEE Transactions on Visualization and Computer Graphics (TVCG), 30, pages 1249-1259, 2024. Supplementary Material. DOI:10.1109/TVCG.2023.3326905 arXiv:2307.15243.
	Hypergraph Co-Optimal Transport: Metric and Categorical Properties. Samir Chowdhury, Tom Needham, Ethan Semrad, Bei Wang, Youjia Zhou. Journal of Applied and Computational Topology, 2023. DOI:10.1007/s41468-023-00142-9 arXiv:2112.03904
	Exploring Gradient Oscillation in Deep Neural Network Training. Chedi Morchdi, Yi Zhou, Jie Ding, Bei Wang. 59th Annual Allerton Conference on Communication, Control, and Computing (ALLERTON), 2023. Allerton 2023 Program
	Comparing Morse Complexes Using Optimal Transport: An Experimental Study. Mingzhe Li, Carson Storm, Austin Yang Li, Tom Needham, Bei Wang. IEEE Visualization and Visual Analytics (VIS) Short Paper, pages 41-45, 2023. Supplementary Material. DOI:10.1109/VIS54172.2023.00017
	From Flowchart to Questionnaire: Increasing Access to Justice via Visualization. Youjia Zhou, Arul Mishra, Himanshu Mishra, Bei Wang IEEE Workshop on Visualization for Social Good (VIS4Good), pages 11-15, 2023. Supplementary Material. DOI:10.1109/VIS4Good60218.2023.00009
	Sketching Merge Trees for Scientific Visualization. Mingzhe Li, Sourabh Palande, Lin Yan, Bei Wang. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS, pages 61-71, 2023. Supplementary Material. DOI:10.1109/TopoInVis60193.2023.00013 arXiv:2101.03196.
	Combinatorial Exploration of Morse-Smale Functions on the Sphere via Interactive Visualization. Youjia Zhou, Janis Lazovskis, Michael J. Catanzaro, Matthew Zabka, Bei Wang. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS, pages 51-60, 2023. DOI:10.1109/TopoInVis60193.2023.00012 arXiv:1912.09580.
	Homology-Preserving Multi-Scale Graph Skeletonization Using Mapper on Graphs. Mustafa Hajij, Bei Wang, Paul Rosen. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS, pages 10-20, 2023. Supplementary Material: User study data. DOI:10.1109/TopoInVis60193.2023.00008 arXiv:1804.11242.
	Comparing Mapper Graphs of Artificial Neuron Activations. Youjia Zhou, Helen Jenne, Davis Brown, Madelyn Shapiro, Brett Jefferson, Cliff Joslyn, Gregory Henselman-Petrusek, Brenda Praggastis, Emilie Purvine, Bei Wang. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS, pages 41-50, 2023. DOI:10.1109/TopoInVis60193.2023.00011
	Metaboverse Enables Automated Discovery and Visualization of Diverse Metabolic Regulatory Patterns. 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. Nature Cell Biology 25, pages 616-625, 2023. Download online. DOI:10.1038/s41556-023-01117-9
	Protein-Metabolite Interactomics of Carbohydrate Metabolism Reveal Regulation of Lactate Dehydrogenase. Kevin G. Hicks, Ahmad A. Cluntun, Heidi L. Schubert, Sean R. Hackett, Jordan A. Berg, Paul G. Leonard, Mariana A. Ajalla Aleixo, Youjia Zhou, Alex J. Bott, Sonia R. Salvatore, Fei Chang, Aubrie Blevins, Paige Barta, Samantha Tilley, Aaron Leifer, Andrea Guzman, Ajak Arok, Sarah Fogarty, Jacob M. Winter, Hee-Chul Ahn, Karen N. Allen, Samuel Block, Iara A. Cardoso, Jianping Ding, Ingrid Dreveny, Clarke Gasper, Quinn Ho, Atsushi Matsuura, Michael J. Palladino, Sabin Prajapati, PengKai Sun, Kai Tittmann, Dean R. Tolan, Judith Unterlass, Andrew P. VanDemark, Matthew G. Vander Heiden, Bradley A. Webb, Cai-Hong Yun, PengKai Zhap, Bei Wang, Francisco J. Schopfer, Christopher P. Hill, Maria Cristina Nonato, Florian L. Muller, James E. Cox, Jared Rutter. Science, 379 (6636), pages 996-1003, 2023. DOI: 10.1126/science.abm3452
	Visualizing and Analyzing the Topology of Neuron Activations in Deep Adversarial Training. Youjia Zhou, Yi Zhou, Jie Ding, Bei Wang. Topology, Algebra, and Geometry in Machine Learning (TAGML) Workshop at ICML, 2023. OpenReview:Q692Q3dPMe.
	VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations. Archit Rathore, Sunipa Dev, Jeff M. Phillips, Vivek Srikumar, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei Zhang, Bei Wang. ACM Transactions on Interactive Intelligent Systems, 14(1), pages 1-34, 2023. DOI: 10.1145/3604433 arXiv:2104.02797.
	Multilevel Robustness for 2D Vector Field Feature Tracking, Selection, and Comparison. Lin Yan, Paul Aaron Ullrich, Luke P. Van Roekel, Bei Wang, Hanqi Guo. Computer Graphics Forum, 42(6), e14799, 2023. DOI: 10.1111/cgf.14799 arXiv:2209.11708
	TopoBERT: Exploring the Topology of Fine-Tuned Word Representations. Archit Rathore, Yichu Zhou, Vivek Srikumar, Bei Wang. Information Visualization, 22(3), pages 186-208, 2023. DOI: 10.1177/14738716231168671
	Visual Computer Cover of the Year 2023. Daniel Klötzl, Tim Krake, Youjia Zhou, Ingrid Hotz, Bei Wang, Daniel Weiskopf. Visual Computer, 2023. Based on Local Bilinear Computation of Jacobi Sets, Visual Computer 38, pages 3435-3448, 2022.
	Meta-diagrams for 2-parameter persistence. Nate Clause, Tamal K. Dey, Facundo Mémoli, Bei Wang. International Symposium on Computational Geometry (SOCG), 2023. DOI:10.4230/LIPIcs.SoCG.2023.25
	Experimental Observations of the Topology of Convolutional Neural Network Activations. Emilie Purvine, Davis Brown, Brett Jefferson, Cliff Joslyn, Brenda Praggastis, Archit Rathore, Madelyn Shapiro, Bei Wang, Youjia Zhou. Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023. DOI: 10.1609/aaai.v37i8.26134 arXiv:2212.00222
	Enable Decision Making for Battery Electric Bus Deployment Using Robust High-Resolution Interdependent Visualization. Gabrielius A. Kudirka, Xinyuan Yan, Sarah Kunzler, Yirong Zhou, Bei Wang, Xiaoyue Cathy Liu. Transportation Research Board (TRB) 102nd Annual Meeting, 2023.
2022
	An interactive visual demo of bias mitigation techniques for word representations from a Geometric Perspective. Archit Rathore, Sunipa Dev, Jeff Phillips, Vivek Srikumar, Yan Zheng, Chin-Chia Michael Yeh, Junpeng Wang, Wei Zhang, Bei Wang. Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track, PMLR,176, pages 330-334, 2022 Online: PMLR
	Humans as Mitigators of Biases in Risk Prediction via Field Studies. Bei Wang, Arul Mishra, Himanshu Mishra. IEEE International Conference on Big Data (IEEE BigData), 2022. DOI: 10.1109/BigData55660.2022.10020306
	Uncertainty Visualization for Graph Coarsening. Fangfei Lan, Sourabh Palande, Michael Young, Bei Wang. IEEE International Conference on Big Data (IEEE BigData), 2022. DOI: 10.1109/BigData55660.2022.10021039
	Local Bilinear Computation of Jacobi Sets Daniel Klötzl, Tim Krake, Youjia Zhou, Ingrid Hotz, Bei Wang, Daniel Weiskopf. Computer Graphics International (CGI), 2022. Visual Computer, 38, pages 3435-3448, 2022. DOI: 10.1007/s00371-022-02557-4 Visual Computer Second Best Paper Award.
	Reduced Connectivity for Local Bilinear Jacobi Sets. Daniel Klötzl, Tim Krake, Youjia Zhou, Jonathan Stober, Kathrin Schulte, Ingrid Hotz, Bei Wang, Daniel Weiskopf. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS, 2022. DOI: 10.1109/TopoInVis57755.2022.00011 arXiv:2208.07148 Honorable Mention Paper Award.
	Untangling Force-Directed Layouts Using Persistent Homology. Bhavana Doppalapudi, Bei Wang, Paul Rosen. IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS, 2022. DOI: 10.1109/TopoInVis57755.2022.00015 arXiv:2208.06927
	Geometry-Aware Merge Tree Comparisons for Time-Varying Data with Interleaving Distances. Lin Yan, Talha Bin Masood, Farhan Rasheed, Ingrid Hotz, Bei Wang. IEEE Transactions on Visualization and Computer Graphics (TVCG), 2022. DOI: 10.1109/TVCG.2022.3163349 (early access) arXiv:2107.14373
	Topological Simplifications of Hypergraphs. Youjia Zhou, Archit Rathore, Emilie Purvine, Bei Wang. IEEE Transactions on Visualization and Computer Graphics (TVCG), 2022. DOI: 10.1109/TVCG.2022.3153895 (early access) arXiv:2104.11214.
	Discrete Stratified Morse Theory: Algorithms and A User's Guide Kevin Knudson and Bei Wang. Discrete & Computational Geometry (DCG), 2022. DOI:10.1007/s00454-022-00372-1
	Uncertainty Visualization of 2D Morse Complex Ensembles Using Statistical Summary Maps. Tushar Athawale, Dan Maljovec, Lin Yan, Chris R. Johnson, Valerio Pascucci, Bei Wang. IEEE Transactions on Visualization and Computer Graphics (TVCG), 28(4), pages 1955-1966, 2022. DOI: 10.1109/TVCG.2020.3022359
	Stitch Fix for Mapper and Topological Gains. Youjia Zhou, Nathaniel Saul, Ilkin Safarli, Bala Krishnamoorthy, Bei Wang. Research in Computational Topology 2, Association for Women in Mathematics Series, vol 30, pages 265-294, Springer, Cham. 2022. Editors: Ellen Gasparovic, Vanessa Robins, Katharine Turner. DOI: 10.1007/978-3-030-95519-9_12
	Graph Pseudometrics from a Topological Point of View. Ana Lucia Garcia-Pulido, Kathryn Hess, Jane Tan, Katharine Turner, Bei Wang, Naya Yerolemou. Research in Computational Topology 2, Association for Women in Mathematics Series, vol 30, pages 99-128, Springer, Cham. 2022. Editors: Ellen Gasparovic, Vanessa Robins, Katharine Turner. DOI: 10.1007/978-3-030-95519-9_5
	Local Versus Global Distances for Zigzag Persistence Modules. Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang, Lori Ziegelmeier. Research in Computational Topology 2, Association for Women in Mathematics Series, vol 30, pages 265-294, Springer, Cham. 2022. Editors: Ellen Gasparovic, Vanessa Robins, Katharine Turner. DOI: 10.1007/978-3-030-95519-9_3 arXiv:1903.08298.
2021
	TopoAct: Visually Exploring the Shape of Activations in Deep Learning. Archit Rathore, Nithin Chalapathi, Sourabh Palande, Bei Wang. Computer Graphics Forum, 40(1), pages 382-397, 2021. Supplemental Material. DOI: 10.1111/cgf.14195 arXiv:1912.06332.
	Adaptive Covers for Mapper Graphs Using Information Criteria. Nithin Chalapathi, Youjia Zhou, Bei Wang. IEEE International Conference on Big Data (IEEE BigData), Workshop on Applications of Topological Data Analysis to Big Data, 2021. DOI: 10.1109/BigData52589.2021.9671324
	Visualization in Astrophysics: Developing New Methods, Discovering Our Universe, and Educating the Earth. Fangfei Lan, Michael Young, Lauren Anderson, Anders Ynnerman, Alexander Bock, Michelle A. Borkin, Angus G. Forbes, Juna A. Kollmeier, Bei Wang. Eurographics Conference on Visualization (EuroVis), 2021. Computer Graphics Forum, 40(3), pages 635-663, 2021. DOI: 10.1111/cgf.14332
	Scalar Field Comparison with Topological Descriptors: Properties and Applications for Scientific Visualization. Lin Yan, Talha Bin Masood, Raghavendra Sridharamurthy, Farhan Rasheed, Vijay Natarajan, Ingrid Hotz, Bei Wang. Eurographics Conference on Visualization (EuroVis), 2021. Computer Graphics Forum, 40(3), pages 599-633, 2021. DOI: 10.1111/cgf.14331
	Pheno-Mapper: An Interactive Toolbox for the Visual Exploration of Phenomics Data. Youjia Zhou, Methun Kamruzzaman, Patrick Schnable, Bala Krishnamoorthy, Ananth Kalyanaraman, Bei Wang. Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB), Article No. 20, pages 1-10, 2021. DOI: 10.1145/3459930.3469511
	Mapper Interactive: A Scalable, Extendable, and Interactive Toolbox for the Visual Exploration of High-Dimensional Data. Youjia Zhou, Nithin Chalapathi, Archit Rathore, Yaodong Zhao, Bei Wang. IEEE Pacific Visualization Symposium, 2021. DOI: 10.1109/PacificVis52677.2021.00021 arXiv:2011.03209.
	A Visual Tour of Bias Mitigation Techniques for Word Representations (Tutorial Overview) Archit Rathore, Sunipa Dev, Jeff M. Phillips, Vivek Srikumar, Bei Wang Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 4064-4065, 2021. DOI: 10.1145/3447548.3470807
	Spatio-Temporal Visualization of Interdependent Battery Bus Transit and Power Distribution Systems. Avishan Bagherinezhad, Michael Young, Bei Wang, Masood Parvania. IEEE PES Innovative Smart Grid Technologies Conference (ISGT), 2021. DOI: 10.1109/ISGT49243.2021.9372185
	Probabilistic Convergence and Stability of Random Mapper Graphs. Adam Brown, Omer Bobrowski, Elizabeth Munch, Bei Wang. Journal of Applied and Computational Topology, 5, pages 99-140, 2021. DOI:10.1007/s41468-020-00063-x arXiv:1909.03488.
	Electrum: Visualization, Analysis, and Contextualization of High-Throughput Protein-Metabolite Interaction Datasets (Abstract). Jordan A. Berg, Ian George, Youjia Zhou, Kevin G. Hicks, Bei Wang, Jared Rutter. Intelligent Systems for Molecular Biology and European Conference on Computational Biology (ISMB/ECCB), 2021.
2020
	Modeling and Identifying Regulatory Patterns within Chaotic Metabolic Networks (Abstract). Jordan A. Berg, Youjia Zhou, Bei Wang, Jared Rutter. Intelligent Systems for Molecular Biology (ISMB), 2020. Youtube Video.
	Towards Spectral Sparsification of Simplicial Complexes Based on Generalized Effective Resistance. Braxton Osting, Sourabh Palande and Bei Wang. Journal of Computational Geometry, 11(1), pages 176-211, 2020. Online: Journal of Computational Geometry. DOI: 10.20382/jocg.v11i1a8 arXiv:1708.08436.
	State of the Art in Time-Dependent Flow Topology: Interpreting Physical Meaningfulness Through Mathematical Properties. Roxana Bujack, Lin Yan, Ingrid Hotz, Christoph Garth, Bei Wang. Eurographics Conference on Visualization (EuroVis) STAR Computer Graphics Forum, 39(3), pages 811-835, 2020. DOI: 10.1111/cgf.14037
	Sheaf-Theoretic Stratification Learning From Geometric and Topological Perspectives. Adam Brown and Bei Wang. Discrete & Computational Geometry, 2020. DOI:10.1007/s00454-020-00206-y arXiv:1712.07734
	On Homotopy Types of Vietoris--Rips Complexes of Metric Gluings. Michal Adamaszek, Henry Adams, Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang and Lori Ziegelmeier. Journal of Applied and Computational Topology, 4, pages 425-454, 2020. DOI:10.1007/s41468-020-00054-y arXiv:1712.06224.
	Moduli Spaces of Morse Functions for Persistence. Michael J. Catanzaro, Justin Curry, Brittany Terese Fasy, Janis Lazovskis, Greg Malen, Hans Riess, Bei Wang, Matthew Zabka. Journal of Applied and Computational Topology, 4, pages 353-385, 2020. DOI:10.1007/s41468-020-00055-x arXiv:1909.10623.
	Mathematical Foundations in Visualization. Ingrid Hotz, Roxana Bujack, Christoph Garth, Bei Wang. In Foundations of Data Visualization, pages 87-119, Springer, 2020 Editors: Min Chen, Helwig Hauser, Penny Rheingans, Gerik Scheuermann. DOI:10.1007/978-3-030-34444-3_5
	Interactive Visualization of Interdependent Power and Water Infrastructure Operation. Han Han, Konstantinos Oikonomou, Nithin Chalapathi, Masood Parvania, Bei Wang. IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2020. DOI:10.1109/ISGT45199.2020.9087680
	Topological Inference of Manifolds with Boundary. Yuan Wang, Bei Wang. Computational Geometry: Theory and Applications, 88(101606), 2020. DOI:10.1016/j.comgeo.2019.101606 arXiv:1810.05759
	Visual Demo of Discrete Stratified Morse Theory (Media Exposition) Youjia Zhou, Kevin Knudson, Bei Wang. International Symposium on Computational Geometry (SoCG), 2020. DOI: 10.4230/LIPIcs.SoCG.2020.82
	Learning With Topological Features of Functional Brain Networks (Abstract). Sourabh Palande, Bei Wang. Algebraic Topology: Methods, Computation, & Science (ATMCS), poster, 2020.
	A Visual Exploration and Design of Morse Vector Fields (Abstract). Youjia Zhou, Janis Lazovskis, Michael J. Catanzaro, Matthew Zabka, Bei Wang. Algebraic Topology: Methods, Computation, & Science (ATMCS), poster, 2020.
	Persistent Homology Guided Force-Directed Graph Layouts. Ashley Suh, Mustafa Hajij, Bei Wang, Carlos Scheidegger, Paul Rosen IEEE Transactions on Visualization and Computer Graphics (TVCG, Proceedings of InfoVis), 26(1), pages 697-707, 2020. DOI: 10.1109/TVCG.2019.2934802 arXiv:1712.05548 Long Video. Short Video.
	A Structural Average of Labeled Merge Trees for Uncertainty Visualization. Lin Yan, Yusu Wang, Elizabeth Munch, Ellen Gasparovic, Bei Wang. IEEE Transactions on Visualization and Computer Graphics (TVCG, Proceedings of SciVis), 26(1), pages 832-842, 2020. Supplemental Material. Doi: 10.1109/TVCG.2019.2934242 arXiv:1908.00113 Long Video. Short Video.

2019
	The Relationship Between the Intrinsic Cech and Persistence Distortion Distances for Metric Graphs. Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang, Lori Ziegelmeier. Journal of Computational Geometry, 10(1), pages 477-499, 2019. DOI: 10.20382/jocg.v10i1a16 arXiv:1812.05282
	A Kernel for Multi-Parameter Persistent Homology. René Corbet, Ulderico Fugacci, Michael Kerber, Claudia Landi, Bei Wang. Shape Modeling International (SMI), 2019. Computers & Graphics: X, 2, 100005, 2019. DOI:10.1016/j.cagx.2019.100005 arXiv:1809.10231 Best Paper Award at SMI 2019!
	Persistence-Driven Design and Visualization of Morse Vector Fields (Extended Abstract) Youjia Zhou, Janis Lazovskis, Michael J. Catanzaro, Matthew Zabka, Bei Wang. China Visualization and Visual Analytics Conference (ChinaVis), 2019. Best Poster Award at ChinaVis 2019!
	Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale. Archit Rathore, Sourabh Palande, Jeffrey Anderson, Brandon Zielinski, Tom Fletcher, Bei Wang. 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019. Supplemental Material DOI:10.1007/978-3-030-32248-9_82
	Robust Extraction and Simplification of 2D Symmetric Tensor Field Topology. Jochen Jankowai, Bei Wang, Ingrid Hotz. Eurographics Conference on Visualization (EuroVis), 2019. Computer Graphics Forum (CGF), 38(3), pages 337-349, 2019. DOI:10.1111/cgf.13693
	Using Contour Trees in the Analysis and Visualization of Radio Astronomy Data Cubes. Paul Rosen, Anil Seth, Betsy Mills, Adam Ginsburg, Julia Kamenetzky, Jeff Kern, Chris R. Johnson, Bei Wang. Topology-Based Methods in Visualization (TopoInVis), 2019. arXiv:1704.04561.
	Topology, Computation and Data Analysis (Dagstuhl Seminar 19212) Editors: Michael Kerber, Vijay Natarajan, Bei Wang. Report from Dagstuhl Seminar, 2019. DOI: 10.4230/DagRep.9.5.110
	Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Statistical Inference. Sourabh Palande, Vipin Jose, Brandon Zielinski, Jeffrey Anderson, P. Thomas Fletcher and Bei Wang. International Workshop on Connectomics in NeuroImaging (CNI) at MICCAI, 2017. Brain Connectivity, 9(1):13-21, 2019 DOI: 10.1089/brain.2018.0604
	Interpreting Galilean Invariant Vector Field Analysis via Extended Robustness. Bei Wang, Roxana Bujack, Paul Rosen, Primoz Skraba, Harsh Bhatia and Hans Hagen. In Topological Methods in Data Analysis and Visualization V: Theory, Algorithms, and Applications (Proceedings of TopoInVis 2017). Springer, 2019. DOI: 10.1007/978-3-030-43036-8.
2018
	Topological Data Analysis of Functional MRI Connectivity in Time and Space Domains. Keri Anderson, Jeffrey Anderson, Sourabh Palande, and Bei Wang. International Workshop on Connectomics in NeuroImaging (CNI) at MICCAI, 2018. Connectomics Neuroimaging (Lecture Notes in Computer Science, Proceedings of International Workshop on Connectomics in NeuroImaging), volume 11083. Springer, 2018. Supplemental Material. DOI: 10.1007/978-3-030-00755-3_8 Best Paper Award at CNI 2018!
	Homology-Preserving Dimensionality Reduction via Manifold Landmarking and Tearing. Lin Yan, Yaodong Zhao, Paul Rosen, Carlos Scheidegger, Bei Wang. Symposium on Visualization in Data Science (VDS) at IEEE VIS, 2018. arXiv:1806.08460
	Discrete Stratified Morse Theory: A User's Guide. Kevin Knudson and Bei Wang. International Symposium on Computational Geometry (SOCG), 2018. DOI: 10.4230/LIPIcs.SoCG.2018.54 arXiv:1801.03183
	Sheaf-Theoretic Stratification Learning. Adam Brown and Bei Wang. International Symposium on Computational Geometry (SOCG), 2018. DOI: 10.4230/LIPIcs.SoCG.2018.14 arXiv:1712.07734
	Vietoris-Rips and Čech Complexes of Metric Gluings. Michal Adamaszek, Henry Adams, Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang and Lori Ziegelmeier. International Symposium on Computational Geometry (SOCG), 2018. DOI: 10.4230/LIPIcs.SoCG.2018.3 arXiv:1712.06224
	Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology. Mustafa Hajij, Bei Wang, Carlos Scheidegger, Paul Rosen. Proceedings IEEE Pacific Visualization Symposium (PacificVis), 2018. DOI: 10.1109/PacificVis.2018.00024 arXiv:1707.06683
	Topology, Computation and Data Analysis (Dagstuhl Seminar 17292) Editors: Hamish Carr, Michael Kerber, and Bei Wang. Report from Dagstuhl Seminar, 2018. DOI: 10.4230/DagRep.7.7.88
	Visual Exploration of Semantic Relationships in Neural Word Embeddings. Shusen Liu, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Vivek Srikumar, Bei Wang, Yarden Livnat and Valerio Pascucci. IEEE Transactions on Visualization and Computer Graphics (TVCG, Proceedings of InfoVis), 24(1):553-562, 2018. DOI:10.1109/TVCG.2017.2745141
	A Complete Characterization of the 1-DimensionalIntrinsic Čech Persistence Diagrams for Metric Graphs. Ellen Gasparovic, Maria Gommel, Emilie Purvine, Radmila Sazdanovic, Bei Wang, Yusu Wang and Lori Ziegelmeier. Research in Computational Topology, Pages 33-56, 2018. DOI: 10.1007/978-3-319-89593-2 arXiv:1512.04108.
2017
	Visualizing Sensor Network Coverage with Location Uncertainty. Tim Sodergren, Jessica Hair, Jeff M. Phillips and Bei Wang. Symposium on Visualization in Data Science (VDS) at IEEE VIS, 2017. DOI: 10.1109/VDS.2017.8573448 ArXiv:1710.06925
	Open Problems in Computational Topology. Brittany Terese Fasy and Bei Wang (with contributions by members of the WinCompTop community). SIGACT NEWS Open Problems Column, Edited by Bill Gasarch, 48(3), 2017. Online Version: SIGACT NEWS open problems column.
	Robustness for 2D Symmetric Tensor Field Topology. Bei Wang and Ingrid Hotz. Modeling, Analysis, and Visualization of Anisotropy, Springer, 2017.
	Exploring the Evolution of Pressure-Perturbations to Understand Atmospheric Phenomena. Wathsala Widanagamaachchi, Alexander Jacques, Bei Wang, Erik Crosman, Peer-Timo Bremer, Valerio Pascucci and John Horel. Proceedings IEEE Pacific Visualization Symposium (PacificVis), 2017. DOI: 10.1109/PACIFICVIS.2017.8031584
	Visualizing High-Dimensional Data: Advances in the Past Decade. Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer and Valerio Pascucci IEEE Transactions on Visualization and Computer Graphics (TVCG), 23(3), pages 1249-1268, 2017. DOI: 10.1109/TVCG.2016.2640960 Survey Website (Maintained by Shusen Liu).
2016
	Visual Exploration of Multiway Dependencies in Multivariate Data. Hoa Nguyen, Paul Rosen and Bei Wang. ACM SIGGRAPH ASIA Symposium on Visualization, pages 1-8, 2016. DOI: 10.1145/3002151.3002162
	Convergence between Categorical Representations of Reeb Space and Mapper. Elizabeth Munch and Bei Wang. International Symposium on Computational Geometry (SOCG)*, 2016. arXiv:1512.04108. Invited Talk at TGDA@OSU.
	Kernel Partial Least Squares Regression for Relating Functional Brain Network Topology to Clinical Measures of Behavior. Eleanor Wong, Sourabh Palande, Bei Wang, Brandon Zielinski, Jeffrey Anderson and P. Thomas Fletcher. International Symposium on Biomedical Imaging (ISBI), 2016. DOI: 10.1109/ISBI.2016.7493506
	Grassmannian Atlas: A General Framework for Exploring Linear Projections of High-Dimensional Data. Shusen Liu, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Bei Wang, Brian Summa and Valerio Pascucci. Eurographics Conference on Visualization (EuroVis), 2016. Computer Graphics Forum (CGF), 35(3), pages 1-10, 2016. DOI: 10.1111/cgf.12876
	Exploring Persistent Local Homology in Topological Data Analysis. Brittany T. Fasy and Bei Wang. Special session on Topological Methods in Data Science and Analysis, IEEE International Conference on Acoustics, Speech and Signal Process (ICASSP)*, 2016.
	Critical Point Cancellation in 3D Vector Fields: Robustness and Discussion. Primoz Skraba, Paul Rosen, Bei Wang, Guoning Chen, Harsh Bhatia and Valerio Pascucci. IEEE Pacific Visualization (PacificVis), 2016. IEEE Transactions on Visualization and Computer Graphics (TVCG), 22(6), pages 1683-1693, 2016. DOI: 10.1109/TVCG.2016.2534538 Supplemental Video. Vortex Video. Best Paper Award at PacificVis 2016!
	Topology-Inspired Partition-Based Sensitivity Analysis and Visualization of Nuclear Simulations. Daniel Maljovec, Bei Wang, Paul Rosen, Andrea Alfonsi, Giovanni Pastore, Cristian Rabiti and Valerio Pascucci. Proceedings IEEE Pacific Visualization (PacificVis), 2016.
	Analyzing Simulation-Based PRA Data Through Traditional and Topological Clustering: A BWR Station Blackout Case Study. Dan Maljovec, Shusen Liu, Bei Wang, Valerio Pascucci, Peer-Timo Bremer, Diego Mandelli and Curtis Smith. Reliability Engineering & System Safety (RESS), 145, pages 262-276, 2016. Online Version: invited longer journal version based on our work from PSAM 2014.
2015
	Reeb Space Approximation with Guarantees (Abstract). Elizabeth Munch and Bei Wang. 25th Annual Fall Workshop on Computational Geometry (FWCG)*, 2015. Proceedings Online.
	Geometric Inference on Kernel Density Estimates. Jeff M. Phillips, Bei Wang and Yan Zheng. International Symposium on Computational Geometry (SOCG), 2015. Conference Proceedings. Full Version: arXiv:1307.7760.
	Local, Smooth, and Consistent Jacobi Set Simplification. Harsh Bhatia, Bei Wang, Gregory Norgard, Valerio Pascucci and Peer-Timo Bremer. Computational Geometry: Theory and Applications (CGTA), 48(4), pages 311-332, 2015. Online Version.
	Interstitial and Interlayer Ion Diffusion Geometry Extraction in Graphitic Nanosphere Battery Materials. Attila Gyulassy, Aaron Knoll, Kah Chun Lau, Bei Wang, Peer-Timo Bremer, Michael E. Papka, Larry A. Curtiss and Valerio Pascucci. Proceedings IEEE Visualization Conference (VIS), 2015. IEEE Transactions on Visualization and Computer Graphics (TVCG), 22(1), pages 916 - 925, 2016.
	Robustness-Based Simplification of 2D Steady and Unsteady Vector Fields. Primoz Skraba, Bei Wang, Guoning Chen and Paul Rosen. IEEE Transactions on Visualization and Computer Graphics (TVCG), 21(8), pages 930 - 944, 2015. Supplemental Material. Supplemental Video.
	Visualizing High-Dimensional Data: Advances in the Past Decade. Shusen Liu, Dan Maljovec, Bei Wang, Peer-Timo Bremer and Valerio Pascucci. Eurographics Conference on Visualization (EuroVis) STAR, 2015. Survey Website.
	Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections. Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer and Valerio Pascucci. Eurographics Conference on Visualization (EuroVis) , 2015. Computer Graphics Forum (CGF), 34(3), pages 271-280, 2015. Supplemental Video. Journal Online. YouTube.
	Morse-Smale Analysis of Ion Diffusion for DFT Battery Materials Simulations. Attila Gyulassy, Aaron Knoll, Kah Chun Lau, Bei Wang, Peer-Timo Bremer, Michael E. Papka, Larry A. Curtiss and Valerio Pascucci. Topology-Based Methods in Visualization (TopoInVis) , 2015.
	ND2AV: N-Dimensional Data Analysis and Visualization -- Analysis for the National Ignition Campaign. Peer-Timo Bremer, Dan Maljovec, Avishek Saha, Bei Wang, Jim Gaffney, Brian K. Spears and Valerio Pascucci. Computing and Visualization in Science (CVS), 17(1), Pages 1-18, 2015. Online Version. Supplementary Video.
2014
	Approximating Local Homology from Samples. Primoz Skraba and Bei Wang. Proceedings 25th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)*, pages 174-192, 2014. SIAM Proceedings Online.
	2D Vector Field Simplification Based on Robustness. Primoz Skraba, Bei Wang, Guoning Chen and Paul Rosen. Proceedings IEEE Pacific Visualization (PacificVis), 2014. PacificVis Supplemental. Best Paper Award at PacificVis 2014!
	Interpreting Feature Tracking Through the Lens of Robustness. Primoz Skraba and Bei Wang. Topological Methods in Data Analysis and Visualization III: Theory, Algorithms, and Applications, pages 19-38, 2014. Workshop Version: Topology-Based Methods in Visualization (TopoInVis)*, 2013.
	Multivariate Volume Visualization through Dynamic Projections. Shusen Liu, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer and Valerio Pascucci. IEEE Symposium on Large Data Analysis and Visualization (LDAV), 2014. LDAV Video. YouTube.
	Distortion-Guided Structure-Driven Interactive Exploration of High-Dimensional Data. Shusen Liu, Bei Wang, Peer-Timo Bremer and Valerio Pascucci. Eurographics Conference on Visualization (EuroVis), 2014. Computer Graphics Forum (CGF), 33(3), pages 101-110, 2014. EuroVis Supplemental. EuroVis Video. Journal Online. YouTube. Software: DataExplorerHD v0.1 (Maintained by Shusen Liu)
	Analyzing Simulation-Based PRA Data Through Clustering: a BWR Station Blackout Case Study. Dan Maljovec, Shusen Liu, Bei Wang, Valerio Pascucci, Peer-Timo Bremer, Diego Mandelli and Curtis Smith. Probabilistic Safety Assessment & Management conference (PSAM), 2014. Proceedings Online.
	Overview of New Tools to Perform Safety Analysis: BWR Station Black Out Test Case. Diego Mandelli, Curtis Smith, Tom Riley, Joseph Nielsen, John Schroeder, Cristian Rabiti, Andrea Alfonsi, Joshua Cogliati, Robert Kinoshita, Valerio Pascucci, Bei Wang, Dan Maljovec. Probabilistic Safety Assessment & Management conference (PSAM) , 2014. Proceedings Online.
2013
	Visualizing Robustness of Critical Points for 2D Time-Varying Vector Fields. Bei Wang, Paul Rosen, Primoz Skraba, Harsh Bhatia, Valerio Pascucci. Eurographics Conference on Visualization (EuroVis) 2013. Computer Graphics Forum (CGF), 32(2), pages 221-230, 2013. EuroVis Supplemental. EuroVis Video. CEDMAV Video. Journal Online.
	Adaptive Sampling with Topological Scores. Dan Maljovec, Bei Wang, Ana Kupresanin, Gardard Johannesson, Valerio Pascucci, Peer-Timo Bremer International Journal for Uncertainty Quantification (IJUQ), 3(2), pages 119-141, 2013. Workshop version: Working with Uncertainty Workshop at IEEE VisWeek, 2011.
	Exploration of High-Dimensional Scalar Function for Nuclear Reactor Safety Analysis and Visualization. Dan Maljovec, Bei Wang, Valerio Pascucci, Peer-Timo Bremer, Michael Pernice, Diego Mandelli and Robert Nourgaliev. Proceedings International Conference on Mathematics and Computational Methods Applied to Nuclear Science & Engineering (M&C), pages 712-723, 2013.
	Adaptive Sampling Algorithms for Probabilistic Risk Assessment of Nuclear Simulations. Dan Maljovec, Bei Wang, Diego Mandelli, Peer-Timo Bremer and Valerio Pascucci. International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA), 2013. First runner-up for the Best Student Paper Award!
	Analyze Dynamic Probabilistic Risk Assessment Data through Clustering. Dan Maljovec, Bei Wang, Diego Mandelli, Peer-Timo Bremer and Valerio Pascucci. International Topical Meeting on Probabilistic Safety Assessment and Analysis (PSA), 2013.
2012
	Local Homology Transfer and Stratification Learning. Paul Bendich, Bei Wang and Sayan Mukherjee. Proceedings 23rd Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), pages 1355-1370, 2012. Full version: arXiv:1008.3572 . A revised journal full version is coming soon!
	Kernel Distance for Geometric Inference (Abstract). Jeff M. Phillips and Bei Wang. 22nd Annual Fall Workshop on Computational Geometry (FWCG)*, 2012.
	Topological Analysis and Visualization of Cyclical Behavior in Memory Reference Traces. A.N.M. Imroz Choudhury, Bei Wang, Paul Rosen and Valerio Pascucci. Proceedings IEEE Pacific Visualization (PacificVis), 2012. Supplemental Video. PacificVis Online.
2011
	Branching and Circular Features in High Dimensional Data. Bei Wang, Brian Summa, Valerio Pascucci and Mikael Vejdemo-Johansson Proceedings IEEE Visualization Conference (VIS), 2011. IEEE Transactions on Visualization and Computer Graphics (TVCG), 17(12), pages 1902-1911, 2011.
	Computing Elevation Maxima by Searching the Gauss Sphere. Bei Wang, Herbert Edelsbrunner and Dmitriy Morozov. Journal of Experimental Algorithmics (JEA), 16, pages 1-13, 2011. Conference Version: Proceedings of the 13th International Symposium on Experimental Algorithms (SEA), 2009; Lecture Notes in Computer Science (LNCS), 5526, pages 281-292, 2009.
2010
	Separating Features from Noise with Persistence and Statistics. Bei Wang. Ph.D. Thesis, Duke University, 2010.
	A Computational Screen for Site Selective A-to-I Editing Detects Novel Sites in Neuron Specific Hu Proteins. Mats Ensterö, Örjan Åkerborg, Daniel Lundin, Bei Wang, Terrence S Furey, Marie Öhman and Jens Lagergren. BMC Bioinformatics, 11(6), 2010.
	Towards Stratification Learning through Homology Inference. Paul Bendich, Sayan Mukherjee and Bei Wang. AAAI Fall Symposium on Manifold Learning and its Applications (AAAI), 2010. Manifold Learning and its Applications: Papers from the AAAI Fall Symposium.
2008
	Spatial Scan Statistics for Graph Clustering. Bei Wang, Jeff M. Phillips, Robert Schrieber, Dennis Wilkinson, Nina Mishra and Robert Tarjan. Proceedings of 8th SIAM International Conference on Data Mining (SDM), 2008.
2007
	Two Proteins for the Price of One: The Design of Maximally Compressed Coding Sequences. Bei Wang, Dimitris Papamichail, Steffen Mueller and Steven Skiena. Natural Computing, 6(4), pages 359-370, 2007. Conference Version: Proceedings of the 11th International Meeting on DNA Computing (DNA), 2005; Lecture Notes in Computer Science (LNCS), 3892, pages 387-398, 2006.
2006
	A Framework for Modeling DNA Based Molecular Systems. Sudheer Sahu, Bei Wang and John H. Reif. Proceedings 12th International Meeting on DNA Computing (DNA), 2006. Lecture Notes in Computer Science (LNCS), 4287, pages 250-265, 2006.

Undergraduate Research

	Experimental Robot Musicians. Tarek M. Sobh, Bei Wang and Kurt W. Coble. Journal of Intelligent and Robotic System (JIRS), 38(2), pages 197-212, 2003. DOI: 10.1023/A:1027319831986
	Web Enabled Robot Design and Dynamic Control Simulation Software Solutions from Task Points Description. Tarek M. Sobh, Bei Wang, and Sarosh H. Patel. Proceedings of the 29th Annual International Conference of the IEEE Industrial Electronics Society (IECON), 2003. DOI: 10.1109/IECON.2003.1280227
	A Mobile Wireless and Web-based Analysis Tool for Robot Design and Dynamic Control Simulation from Task Points Description. Tarek M. Sobh, Bei Wang and Sarosh Patel. Journal of Internet Technology, 4(3), pages 153-161, 2003.
	Web Based Remote Surveillance of Mobile Robot. Tarek M. Sobh, Rajeev Sanyal and Bei Wang. Journal of Internet Technology, 4(3), pages 179-184, 2003.

Miscellaneous

Topology-Based Active Learning.
Dan Maljovec, Bei Wang, John Moeller and Valerio Pascucci.
SCI Technical Report UUSCI-2014-00, 2014.

A Comparative Study of Morse Complex Approximation Using Different Neighborhood Graphs.
Dan Maljovec, Avishek Saha, Peter Lindstrom, Peer-Timo Bremer, Bei Wang, Carlos Correa, and Valerio Pascucci.
Topology-Based Methods in Visualization (TopoInVis), 2013.

Invited Talks

Department of Computer Science, Aarhus University, May 31, 2024.
Capturing Robust Topology in Data.
NII Shonan Meeting on Advancing Visual Computing in Materials Science, Japan, May 14, 2024.
Topological Data Analysis for Materials Science: A Hypergraph Perspective.
Data Science and Applied Topology Seminar at the City University of New York, April 5, 2024.
Reeb Graphs and Measure Theoretic Variants.
Overview Talk: Dagstuhl Seminar 24092: Applied and Combinatorial Topology, Dagstuhl, Germany, Feb 26, 2024.
Reeb Graphs and Their Variants: Theory and Application.
MPI Geometry Seminar, Max Planck Institute for Mathematics in the Sciences, Berlin, Germany, Jan 23, 2024.
Reeb Graphs and Measure Theoretic Variants: Theory and Applications.
MATH+ Workshop on Small Data Analysis, Zuse Institute Berlin (ZIB), Leipzig, Germany, Jan 17, 2024.
Reeb Graphs and Measure Theoretic Variants: Theory and Applications.
Computational Topology and Application Workshop at Tsinghua Sanya International Mathematics Forum (TSIMF), Dec 18, 2023.
Measure Theoretic Reeb Graphs and Reeb Spaces with Applications.
BSV Research Seminar on Computer Graphics, Image Processing, and Visualization, November 8, 2023.
Complex data visualization: climate simulations, high-dimensional point clouds, hypergraphs and beyond.
Asia Pacific Seminar on Applied Topology and Geometry (APATG), October 27, 2023.
Hypergraph Co-Optimal Transport: Metric and Categorical Properties.
Computational Oncology Research Initiative (CORI) Speaker Series, October 23, 2023.
Visualizing Metabolic Networks and Beyond.
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, Oct 19, 2023.
Topology of Artificial Neuron Activations in Deep Learning.
Dagstuhl Seminar on Computational Geometry of Earth System Analysis, August 21, 2023.
Topological Characterization and Uncertainty Visualization of Atmospheric Rivers.
International Forum at the China Visualization and Visual Analytics Conference (ChinaVis), July 21, 2023.
Los Alamos National Lab (LANL) Data Science at Scale (DSI) Summer School Speaker Series, June 27, 2023.
Topology of Artificial Neuron Activations: From Images to Word Embeddings.
Lecture at CoE Hi-GEAR summer camp, June 26, 2023.
Topological Data Analysis and Visualization: Understanding the Shape of Data.
Oxford Applied Topology seminar, June 16, 2023.
Topology of Artificial Neuron Activations: From Images to Word Embeddings.
7th Workshop on Geometry and Machine Learning at SoCG, June 15, 2023.
Topology of Artificial Neuron Activations: From Images to Word Embeddings.
AI Seminar at ScaDS.AI (Center for Scalable Data Analytics and Artificial Intelligence), Leipzig University, May 15, 2023.
Dagstuhl seminar on Universals of Linguistic Idiosyncrasy in Multilingual Computational Linguistics, May 12, 2023.
Colorado State University Topology Seminar, April 18, 2023.
Northeastern Topology Seminar, April 11, 2023.
Institute for Mathematical and Statistical Innovation (IMSI), Randomness in Topology and its Applications workshop, March 21, 2023.
Keynote: Machine Learning on Higher-Order Structured data (ML-HOS) Workshop at ICDM 2022. Hypergraph Co-Optimal Transport, November 28, 2022.
Dagstuhl Seminar on Set Visualization and Uncertainty, Germany. Visualizing Hypergraphs With Connections to Uncertainty Visualization. November 13-18, 2022.
Stochastic Seminar, Department of Mathematics, University of Utah, November 4, 2022.
Mini Symposium on Statistics and Machine Learning in Topological and Geometric Data Analysis at SIAM Conference on Mathematics of Data Science (MDS22), September 29, 2022.
Department of Energy Computer Graphics Forum, August 30, 2022.
Utah Center for Data Science (UCDS) Data Science Seminar, August 24, 2022.
Applied Topology in Frontier Sciences. Applied, Combinatorial and Toric Topology. Institute for Mathematical Sciences, Singapore, July 18 to 22, 2022.
Spring Western AMS Sectional Meeting, special session on Computational Topology and Applications, May 14-15, 2022.
Women in Data Science Ames Regional Event at the Iowa State University, April 21, 2022.
University of Iowa Mathematical Biology Seminar, April 18, 2022.
Colloquium Talk at Department of Computational Mathematics, Science, and Engineering (CMSE), Michigan State University, April 4, 2022.
Joint Mathematics Meetings AMS Special Session on Combinatorial Approaches to Topological Structures and Applications, April 9, 2022.
Joint Mathematics Meetings AWS Special Session on Women in Computational Topology, April 9, 2022.
Visualization Seminar at the University of Utah, March 23, 2022.
Workshop on Algebraic Combinatorics and Category Theory in Topological Data Analysis, March 12, 2022.
Institute for Mathematical and Statistical Innovation (IMSI), the Mathematics of Soft Matter Structure and Dynamics workshop, February 28, 2022.
TDA Week, Japan, February 18, 2022.
Distinguished Seminar Speaker: SIAM Pacific Northwest (PNW) Distinguished Seminar, February 15, 2022.
Computational Persistence Workshop, November 3, 2021.
Seminar GEOTOP-A: Applications of geometry and topology, August 20, 2021.
ILJU Pohang University of Science Technology (POSTECH) Mathematical Institute for Data Science (MINDS) Workshop on Topological Data Analysis and Machine Learning, South Korea, July 7, 2021.
SIAM Conference on Applications of Dynamical Systems (DS21), Mini-symposium on Topological Signal Processing, May 26, 2021.
MSRI (Mathematical Sciences Research Institute) Hot Topics: Topological Insights In Neuroscience, May 2021.
Applied Algebraic Topology Research Network (AATRN) Vietoris-Rips Seminar, May 2021.
Geometry-Topology Seminar, Oregon State University, May 24, 2021.
Computational Mathematics, Science and Engineering (CMSE) Colloquiums, Michigan State University, April . 2021.
Meldrum Science Seminar Series, Westminster College, April, 2021.
CAM Colloquium, Committee on Computational and Applied Mathematics (CCAM), University of Chicago, Mar., 2021.
Pacific Northwest National Laboratory (PNNL) Mathematics for Artificial Reasoning in Science (MARS) Seminar Series, Jan. 2021.
Joint Mathematics Meetings (JMM) AMS Special Session on Combinatorial Approaches to Topological Structures, Jan. 2021.
Applied Topology Seminar at Swiss Federal Institute of Technology Lausanne (EPFL), Nov. 2020.
Machine Learning Seminar at Florida State University, Oct. 2020.
High-Performance Computing (HPC) China Seminar, Sep. 2020.
MBI Optimal Transport Workshop: Optimal Transport, Topological Data Analysis and Applications to Shape and Machine Learning, Jul., 2020.
GAMES: Graphics And Mixed Environment Seminar, Jul., 2020.
Applied Algebraic Topology Research Network, May, 2020.
Joint Mathematics Meetings (JMM) Special Session on Applied Topology, Jan. 2020.
American Mathematical Society (AMS) Sectional Meeting at University of Florida in Gainesville FL, Nov. 2019.
Dagstuhl seminar: Topology, Computation and Data Analysis, May 2019.
JMM AMS-AWM Special Session on Women in Applied and Computational Topology, Jan., 2019.
VISA Research, Dec., 2018.
Dagstuhl seminar: Visualization and Processing of Anisotropy in Imaging, Geometry, and Astronomy, Nov. 2018.
ICERM TRIPODS Summer Bootcamp: Topology and Machine Learning, Aug. 2018.
CG Week 3rd Workshop on Geometry and Machine Learning, Jun. 2018.
IMA Workshop Bridging Statistics and Sheaves, May 2018.
NII Shonan Meeting Seminar 122 Analyzing Large Collections of Time Series, Feb. 2018.
Discrete Math Seminar Talk, University of South Florida, Oct. 2017.
Math Department Colloquium, University of South Florida, Oct. 2017.
Topology Seminar Talk, University of Florida, Oct. 2017.
Interdisciplinary Data Science Consortium, University of South Florida, Oct. 2017.
BIRS Workshop: Topological Data Analysis: Developing Abstract Foundations, Jul. 2017.
Dagstuhl seminar: Computational Geometry, April . 2017.
BIRS Workshop: Topological Methods in Brain Network Analysis, May. 2017.
Topological Data Analysis and Related Topics (TDART), AIMR Tohoku University Advanced Institute for Materials Research, Feb. 2017.
Distinguished lecture: Excellence Center at Linkoping - Lund on Information Technology (ELLIIT) distinguished lecture, Linkoping University, Sweden, May. 2016.
Topology, Geometry, and Data Analysis Conference at Ohio State University, May. 2016.
Pacific Northwest National Laboratory, 2015.
Topological Data Analysis and Visualization: from Vector Fields to High-Dimensional Data.
SAMSI workshop on Topological Data Analysis, research program on Low Dimensional Structure in High Dimensional Systems, 2014.
Geometric Inference on Kernel Density Estimates.
Computer Science Department, Ohio State University, 2014.
Robustness-Based Vector Fields Simplification & Topology-Based Active Learning.
Computer Science Department Colloquium, University of Connecticut, 2013.
Vector Field Visualization and Simplification based on Robustness.
Colloquium Series in School of Engineering, University of Bridgeport, 2013.
Topological Data Analysis and Visualization: A Biased and Incomplete Point of View.
IMA Workshop on Modern Applications of Homology and Cohomology, 2013.
Homology and Cohomology in Visualization: From Vector Fields to Memory Reference Traces.
Organizer and Speaker: PSA Technical Workshop on Topological Data Analysis and Visualization for Large-Scale and High-Dimensional Science Discovery, 2013.
Topological Data Analysis and Visualization for Large-Scale and High-Dimensional Science Discovery.
Mini-symposium on Applied and Computational Topology, SIAM Conference on Applied Algebraic Geometry (AG), 2013.
Geometric Inference on Kernel Density Estimates.
AMS-MAA Joint Mathematics Meeting (JMM), special session on Computational and Applied Topology, 2012.
Stratification Learning through Local Homology Transfer.
Theory Lunch, School of Computer Science, Carnegie Mellon University, 2012.
Towards Stratification Learning through Local Homology Transfer.
Applied Math Seminar, Department of mathematics, University of Utah, 2012.
Stratification Learning through Local Homology Transfer.
Yaroslavl international conference Discrete Geometry dedicated to centenary of A. D. Alexandrov, Russia, 2012.
Stratification Learning.
Summer school of the Delaunay Laboratory, Russia, 2012. Study of the Elevation function.
ACM Symposium on Computational Geometry (SOCG) Workshop on Computational Topology, 2012.
Sampling for Local Homology with Vietoris-Rips Complexes.
Fields Institute for Research in Mathematical Sciences, Thematic Program on Discrete Geometry and Applications, Workshop on Computational Topology, 2011.
Stratification Learning through Local Homology Transfer.

Extra

I am always open to discussions on topology, geometry, mathematics, biology, food, and everything in between.
I have a travel blog Jumpy Shell, a travel instagram, and a food blog Bei's Bites, all of which have been more or less inactive because of these kids.
I am married to Jeff M. Phillips, and we have two sons.

I grew up in Chengdu, Sichuan, China.
I came to US after graduating from Chengdu No.7 High School.
My high school celerated its 110 year anniversary in 2015.
Chengdu is a city of gastronomy (as declared by UNESCO in 2011).
It is famous for many things I love, including spicy food, a Giant Sitting Buddha and pandas.