Bei Wang

Research Computer Scientist

Scientific Computing and Imaging Institute
University of Utah
Warnock Engineering Building (WEB) Room 4819
72 South Central Campus Drive
Salt Lake City, Utah 84112

Email: beiwang AT sci.utah.edu

Office Phone: (801) 581-8957

Short Bio

I am a research scientist at the Scientific Computing and Imaging (SCI) Institute of the University of Utah. I am part of the research group lead by Valerio Pascucci . I am also part of the Center for Extreme Data Management Analysis and Visualization (CEDMAV) . I did my Ph.D. in Computer Science at Duke University with Herbert Edelsbrunner. I also obtained a certificate in Computational Biology and Bioinformatics. During the fall semester of 2009, I was at the Institute of Science and Technology Austria (IST Austria).

Research Interests

Computational topology, computational geometry, scientific data analysis and visualization, computational biology and bioinformatics, machine learning, data mining, molecular modeling and simulation.

I am primarily interested in topological data analysis and visualization. The following are some examples of my recent research activities.

Stratification Learning: Recently I concern myself with combining topological and machine learning techniques in understanding scientific datasets. I ask the following questions: Given (potentially high-dimensional) point cloud samples, can we infer the topological or geometric structure of the underlying data? Often we assume the support of the domain is either from a low-dimensional space with manifold structure, or more interestingly, contains mixed dimensionality and complexity. The former is a classic setting in manifold learning. The latter can often be described by a stratified set of manifolds and becomes a problem of particular interest in the field of stratification learning.

Adaptive Sampling: Dynamic Probabilistic Risk Assessment (PRA) and uncertainty quantification (UQ) of complex systems such as nuclear simulations usually employ sampling algorithms which perform series of computationally expensive simulation runs given a large set of uncertainty parameters. Consequently, the space of the possible solutions, the response surface, can be sampled only very sparsely and this precludes the ability to fully analyze the impact of uncertainties on the system dynamics. Adaptive sampling algorithms aim to overcome these limitations by sampling unexplored and risk-significant regions of the response surface. They infer system responses from surrogate models constructed from existing samples and suggest the most relevant location of the next sample. We aim to develop advanced adaptive sampling techniques to understand the response space locally and globally, drawing inspirations from topology, geometry and machine learning.

High-dimensional Data Analysis and Visualization: Understanding and describing expensive black box functions such as physical simulations is a common problem in many application areas. One example is the recent interest in uncertainty quantification with the goal of discovering the relationship between a potentially large number of input parameters and the output of a simulation. We model large-scale physical simulation datasets as a high-dimensional scalar function defined over a discrete sample of the domain. First, we provide structural analysis of such a function at multiple scales and provide insight into the relationship between the input parameters and the output. Second, we enable exploratory analysis for users, where we help the users to differentiate features from noise through multi-scale analysis on an interactive platform, based on domain knowledge and data characterization. Our analysis is performed by exploiting the topological and geometric properties of the domain, building statistical models based on its topological segmentations and providing interactive visual interfaces to facilitate such explorations.

Recent Research Activities

Continuity Preserving Jacobi Set Simplification.
Harsh Bhatia, Bei Wang, Gregory Norgard, Valerio Pascucci and Peer-Timo Bremer, 2014.
arXiv:1307.7752. New arXiv version coming soon!
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, 2013.
PDF Kernel Distance for Geometric Inference.
Jeff M. Phillips and Bei Wang, 2013.
arXiv:1307.7752. New arXiv version coming soon!

Recent Publications

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) (accepted), 2014.
PDF 2D Vector Field Simplification Based on Robustness.
Primoz Skraba, Bei Wang, Guoning Chen and Paul Rosen.
IEEE Pacific Visualization (PacificVis) , 2014. Best Paper!
PDF PacificVis Supplemental.
PDF 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.
PDF Visualizing Robustness of Critical Points for 2D Time-Varying Vector Fields.
Bei Wang, Paul Rosen, Primoz Skraba, Harsh Bhatia and Valerio Pascucci.
Eurographics Conference on Visualization (EuroVis) 2013.
Computer Graphics Forum, 32(2), pages 221-230, 2013.
PDF EuroVis Supplemental. EuroVis Video. CEDMAV Video. Journal Online.
PDF 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.
PDF 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.
PDF 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.
PDF Interpreting Feature Tracking Through the Lens of Robustness.
Primoz Skraba and Bei Wang.
TopoInVis, 2013.
Topological Methods in Data Analysis and Visualization III: Theory, Algorithms, and Applications (accepted), 2013.
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.
TopoInVis, 2013. Full version coming soon!
PDF Adaptive Sampling with Topological Scores.
Dan Maljovec, Bei Wang, Ana Kupresanin, Gardard Johannesson, Valerio Pascucci, Peer-Timo Bremer
Working with Uncertainty Workshop at IEEE VisWeek, 2011.
International Journal for Uncertainty Quantification , 3(2), pages 119-141, 2013.
PDF Kernel Distance for Geometric Inference (Abstract).
Jeff M. Phillips and Bei Wang
22nd Annual Fall Workshop on Computational Geometry, 2012.
PDF Topological Analysis and Visualization of Cyclical Behavior in Memory Reference Traces.
A.N.M. Imroz Choudhury, Bei Wang, Paul Rosen and Valerio Pascucci.
IEEE Pacific Visualization (PacificVis), 2012. Video. PacificVis Online.
PDF 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!
PDF Branching and Circular Features in High Dimensional Data.
Bei Wang, Brian Summa, Valerio Pascucci and Mikael Vejdemo-Johansson
IEEE Transactions on Visualization and Computer Graphics , 17(12), pages 1902-1911, 2011.
Towards Stratification Learning through Homology Inference.
Paul Bendich, Sayan Mukherjee and Bei Wang.
AAAI 2010 Fall Symposium on Manifold Learning and its Applications, 2010. Manifold Learning and its Applications: Papers from the AAAI Fall Symposium.
PDF Separating Features from Noise with Persistence and Statistics.
Bei Wang.
Ph.D. Thesis, Duke University, 2010.
PDF Computing Elevation Maxima by Searching the Gauss Sphere.
Bei Wang, Herbert Edelsbrunner and Dmitriy Morozov.
Proceedings of the 13th International Symposium on Experimental Algorithms, 2009. Lecture Notes in Computer Science, 5526, pages 281-292, 2009. ACM Journal of Experimental Algorithmics, 16, pages 1-13, 2011.
PDF 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.
PDFSpatial Scan Statistics for Graph Clustering.
Bei Wang, Jeff M. Phillips, Robert Schrieber, Dennis Wilkinson, Nina Mishra and Robert Tarjan.
Proceedings of 8th SIAM Intenational Conference on Data Mining, 2008.
PDFA Framework for Modeling DNA Based Molecular Systems.
Sudheer Sahu, Bei Wang and John H. Reif.
Lecture Notes in Computer Science, 4287, pages 250-265, 2006.
PDFTwo Proteins for the Price of One: The Design of Maximally Compressed Coding Sequences.
Bei Wang, Dimitris Papamichail, Steffen Mueller and Steven Skiena.
Proceedings of the 11th International Meeting on DNA Based Computers, 2005. Lecture Notes in Computer Science, 3892, pages 387-398, 2006. Also in Natural Computing, 2006.
PDFExperimental Robot Musicians.
Tarek M. Sobh, Bei Wang and Kurt W. Coble.
Journal of Intelligent and Robotic System, 38(2), pages 197-212, 2003.

Recent Talks and Travel

PDFHomology and Cohomology in Visualization: From Vector Fields to Memory Reference Traces.
IMA Workshop on Modern Applications of Homology and Cohomology, 2013.

PDF Topological Data Analysis and Visualization for Large-Scale and High-Dimensional Science Discovery
PSA Technical Workshop on Topological Data Analysis and Visualization for Large-Scale and High-Dimensional Science Discovery, 2013 (Organizer)

22nd Annual Fall Workshop on Computational Geometry 2012

CMU Theory Lunch 2012

Applied Math Seminar, Department of mathematics, University of Utah 2012

Yaroslavl international conference Discrete Geometry dedicated to centenary of A.D.Alexandrov 2012

Summer school of the Delaunay Laboratory 2012 (Lecturer)

Workshop on Computational Topology at ACM Symposium on Computational Geometry (SOCG) 2012

SAMSI Uncertainty Quantification Transition Workshop 2012 (Poster)

IEEE Pacific Visualization Symposium 2012

ACM-SIAM Symposium on Discrete Algorithms (SODA) 2012

Extra

I am always open to discussions on topology, geometry, biology, food, and everything in between.

I have a travel blog Jumpy Shell and a food blog Bei's Bites .