Haibin Hang

Mathematics & Data Analysis

Haibin Hang

I hold a Ph.D. in applied mathematics from Florida State University and a master’s degree from Capital Normal University. My research spans pure mathematics, applied mathematics, and deep learning. I enjoy turning mathematical ideas into practical tools for computer vision and scientific data analysis. I use Python, Rust, MATLAB, and C++ to implement and test these methods.

Portrait of Haibin Hang

Research Interests

Topological and geometric data analysis; object-oriented data analysis; statistics on graphs and manifolds; scientific and practical applications and low-dimensional topology.

Publications

  1. Adaptive Feature Interpolation for Low-Shot Image Generation

    M. Dai, H. Hang, X. Guo

    Accepted by European Conference on Computer Vision (ECCV) (2022)

    arXiv

  2. Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks

    M. Dai, H. Hang, A. Srivastava

    Proceedings of the 39th International Conference on Machine Learning Workshops (AdvML) (2022)

    arXiv

  3. U-match factorization: sparse homological algebra, lazy cycle representatives, and dualities in persistent(co)homology

    H. Hang, C. Giusti, L. Ziegelmeier, G. Henselman

    ArXiv:2108.08831 (2021).

    arXiv

  4. Manifold Matching via Deep Metric Learning for Generative Modeling

    M. Dai, H. Hang

    Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)

    ICCV 2021 Code

  5. Decorated merge trees for persistent topology

    J. Curry, H. Hang, W. Mio, T. Needham, O. Okutan

    Journal of Applied and Computational Topology (2022), 1-58

    DOI Code

  6. Detecting Carbon Nanotube Orientation with Topological Analysis of Scanning Electron Micrographs

    L. Dong, H. Hang, J. G. Park, W. Mio, R. Liang

    Nanomaterials (2022)

    Slides DOI Code

  7. Geometric and topological approaches to shape variation in Ginkgo leaves

    H. Hang, M. Bauer, W. Mio, L. Mander

    Royal Society open science (2021)

    DOI Code

  8. Correspondence modules and persistence sheaves: a unifying perspective on one-parameter persistent homology

    H. Hang, W. Mio

    Japan Journal of Industrial and Applied Mathematics (2022).

    DOI

  9. A topological study of functional data and Fréchet functions of metric measure spaces

    H. Hang, F. Mémoli, W. Mio

    J Appl. and Comput. Topology (2019).

    DOI arXiv

  10. Covariance tensors on Riemannian manifolds

    H. Hang, F. Mémoli, W. Mio

    Oberwolfach Reports, Workshop on Statistics for Data with Geometric Structure (2018), 153-156.

    DOI Slides

  11. Homology and orientation reversing periodic maps on surfaces

    H. Hang

    Topology and its Applications, 229 (2017), 1-19.

    DOI arXiv

Contact

Email
haibin dot hang312 at gmail dot com
Webpage
haibinhang.github.io
ResearchGate
researchgate.net/profile/Haibin-Hang
Google Scholar
scholar.google.com/citations?user=SO-5E0MAAAAJ&hl=en
GitHub
github.com/haibinhang