Lin Yan (颜琳)

Assistant Professor. Department of Computer Science. Iowa State University

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Web page last update:

February 01, 2024.

Department of Computer Science
Iowa State University
101 Atanasoff Hall
2434 Osborn Dr
Ames, IA, 50011

Email: linyan@iastate.edu

Short Biography: Dr. Lin Yan is currently an assistant professor in the Department of Computer Science at Iowa State University. She received her Ph.D. in computing from the University of Utah in 2022, under the supervision of Prof. Bei Wang Phillips. She received her BS and MS from Shanghai Jiao Tong University. Her research interests include topological data analysis and visualization. Her recent work focuses on problems involving large and complex forms of data by combining topological, and statistical data analysis, machine learning, and visualization techniques.

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

News

Jan 1, 2024 Become an assistant professor at Iowa State University.
Nov 11, 2023 Had her little girl, Evelyn Han, born 😊 😊
Jul 15, 2023 Two papers accepted by IEEE VIS 2023 as the leading author! :smile: :smile:
Jun 27, 2022 Become a post-doc at Argonne National Laboratory.
Apr 12, 2022 Successfully defended her Ph.D. dissertation! :sparkles: :sparkles:

Recent Selected Publications

2023

  1. MRF.png
    Multilevel Robustness for 2D Vector Field Feature Tracking, Selection, and Comparison
    Lin Yan, Paul Aaron Ullrich, Luke P Van Roekel, Bei Wang, and Hanqi Guo
    Computer Graphics Forum, 2023
    arXiv DOI PDF
  2. TROPHY.png
    TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones
    Lin Yan, Hanqi Guo, Tom Peterka, Bei Wang, and Jiali Wang
    IEEE Transactions on Visualization and Computer Graphics (TVCG, Proceedings of IEEE Visualization Conference), 2023
    arXiv PDF
  3. TopoSZ.png
    TopoSZ: Preserving Topology in Error-Bounded Lossy Compression
    Lin Yan, Xin Liang, Hanqi Guo, and Bei Wang
    IEEE Transactions on Visualization and Computer Graphics (TVCG, Proceedings of IEEE Visualization Conference), 2023
    arXiv PDF
  4. SketchingMT.png
    Sketching Merge Trees for Scientific Visualization
    Mingzhe Li, Sourabh Palande, Lin Yan, and Bei Wang
    IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis) at IEEE VIS, accepted, 2023
    arXiv PDF

2022

  1. geometryAware.png
    Geometry-Aware Merge Tree Comparisons for Time-Varying Data With Interleaving Distances
    Lin Yan, Talha Bin Masood, Farhan Rasheed, Ingrid Hotz, and Bei Wang
    IEEE Transactions on Visualization and Computer Graphics, 2022
    arXiv DOI PDF Code

2021

  1. STAR-MT.png
    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, and Bei Wang
    Computer Graphics Forum, Proceedings of Eurographics Conference on Visualization (EuroVis) STAR, 2021
    arXiv DOI PDF

2020

  1. UncertianMC.png
    Uncertainty visualization of 2D Morse complex ensembles using statistical summary maps
    Tushar M Athawale, Dan Maljovec, Lin Yan, Chris R Johnson, Valerio Pascucci, and Bei Wang
    IEEE Transactions on Visualization and Computer Graphics, 2020
    DOI PDF
  2. STAR-timeVF.png
    State of the art in time-dependent flow topology: Interpreting physical meaningfulness through mathematical properties
    Roxana Bujack, Lin Yan, Ingrid Hotz, Christoph Garth, and Bei Wang
    Computer Graphics Forum, Proceedings of Eurographics Conference on Visualization (EuroVis) STAR, 2020
    DOI PDF

2019

  1. AMT.png
    A structural average of labeled merge trees for uncertainty visualization
    Lin Yan, Yusu Wang, Elizabeth Munch, Ellen Gasparovic, and Bei Wang
    IEEE Transactions on Visualization and Computer Graphics (TVCG, Proceedings of IEEE Visualization Conference), 2019
    arXiv DOI PDF Code

2018

  1. HIsomap.png
    Homology-preserving dimensionality reduction via manifold landmarking and tearing
    Lin Yan, Yaodong Zhao, Paul Rosen, Carlos Scheidegger, and Bei Wang
    Symposium on Visualization in Data Science (VDS) at IEEE VIS, 2018
    arXiv PDF Code