Lin Yan (颜琳)

Assistant Professor. Department of Computer Science. Iowa State University

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Web page last update:

June 8, 2026.

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.

Students:

News

Jun 5, 2026 Congratulations to my students, Shakiba and Ilia, on the acceptance of their first paper in IEEE Transactions on Visualization and Computer Graphics (TVCG)!
May 14, 2026 Congratulations to my student Elnaz on receiving a Major Revision decision for her paper in NPJ Climate and Atmospheric Science (Nature Portfolio).
Oct 14, 2024 My Ph.D. thesis won the IEEE VGTC Visualization Doctoral Dissertation Award! 😎 😎 😎 😎
Jan 1, 2024 Become an assistant professor at Iowa State University.
Nov 11, 2023 Had her little girl, Evelyn Han, born 😊 😊

Recent Selected Publications

2026

  1. S2S.png
    Dual-Scale Temporal Fusion Reveals Structured Predictability in Subseasonal-to-Seasonal Temperature Prediction
    Elnaz Bashir, Jiali Wang, and Lin Yan
    NPJ Climate and Atmospheric Science (Major Revision), 2026
    arXiv PDF
  2. AdaHIsomap.png
    Geometry-Aware Merge Tree Comparisons for Time-Varying Data With Interleaving Distances
    Shakiba Khourashahi, Ilia Jahanshahi, Bei Wang, and Lin Yan
    IEEE Transactions on Visualization and Computer Graphics (accepted), 2026
    arXiv PDF Code

2025

  1. TG.png
    A General Framework for Augmenting Lossy Compressors with Topological Guarantees
    Nathaniel Gorski, Xin Liang, Hanqi Guo, Lin Yan, and Bei Wang
    IEEE Transactions on Visualization and Computer Graphics (TVCG, IEEE Pacific Visualization Symposium (PacificVis) TVCG Journal Track), 2025
    PDF
  2. VGTC.png
    Topology-Based Visualization Techniques for Scientific Data Exploration
    Lin Yan
    IEEE Computer Graphics and Applications, 2025
  3. GWMT.png
    Flexible and probabilistic topology tracking with partial optimal transport
    Mingzhe Li, Xinyuan Yan, Lin Yan, Tom Needham, and Bei Wang
    IEEE Transactions on Visualization and Computer Graphics, 2025
    PDF

2024

  1. MSz.png
    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, and Hanqi Guo
    IEEE Transactions on Visualization and Computer Graphics (TVCG, Proceedings of IEEE Visualization Conference), 2024
    arXiv PDF
  2. AR.png
    Topological Characterization and Uncertainty Visualization of Atmospheric Rivers
    Fangfei Lan, Brandi Gamelin, Lin Yan, Jiali Wang, Bei Wang, and Hanqi Guo
    Computer Graphics Forum, Proceedings of Eurographics Conference on Visualization (EuroVis), 2024
    PDF

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