Haibin Hang

I have enthusiasm for applying mathematics to practical problems across diverse disciplines. Previous collaborations with scientists in biology, material science, etc. enhanced my ability in data analysis, computer vision, and machine learning. Besides my theoretical and analytical background, I am also capable of implementing my ideas using programming languages like Python, Matlab, Julia, Rust, and C++.

Curriculum Vitae
Research Statement

Research Interests

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

Contact

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

Publications

  1. M. Dai, H. Hang, X. Guo, Adaptive Feature Interpolation for Low-Shot Image Generation, Accepted by European Conference on Computer Vision (ECCV) (2022) [arXiv]
  2. M. Dai, H. Hang, A. Srivastava, Rethinking Multidimensional Discriminator Output for Generative Adversarial Networks, Proceedings of the 39th International Conference on Machine Learning Workshops (AdvML) (2022) [arXiv]
  3. H. Hang, C. Giusti, L. Ziegelmeier, G. Henselman, U-match factorization: sparse homological algebra, lazy cycle representatives, and dualities in persistent(co)homology. ArXiv:2108.08831 (2021). [arXiv]
  4. M. Dai, H. Hang, Manifold Matching via Deep Metric Learning for Generative Modeling. Proceedings of the IEEE/CVF International Conference on Computer Vision (2021) [ICCV2021] [code]
  5. J. Curry, H. Hang, W. Mio, T. Needham, O. Okutan, Decorated merge trees for persistent topology, Journal of Applied and Computational Topology (2022), 1-58 [doi] [code]
  6. L. Dong, H. Hang, J. G. Park, W. Mio, R. Liang, Detecting Carbon Nanotube Orientation with Topological Analysis of Scanning Electron Micrographs, Nanomaterials (2022) [slides] [doi] [code]
  7. H. Hang, M. Bauer, W. Mio, L. Mander, Geometric and topological approaches to shape variation in Ginkgo leaves, Royal Society open science (2021) [doi] [code]
  8. H. Hang, W. Mio, Correspondence modules and persistence sheaves: a unifying perspective on one-parameter persistent homology, Japan Journal of Industrial and Applied Mathematics (2022). [doi]
  9. H. Hang, F. Mémoli, W. Mio, A topological study of functional data and Fréchet functions of metric measure spaces. J Appl. and Comput. Topology (2019). [doi] [arXiv]
  10. H. Hang, F. Mémoli, W. Mio, Covariance tensors on Riemannian manifolds, Oberwolfach Reports, Workshop on Statistics for Data with Geometric Structure (2018), 153-156. [doi] [slides]
  11. H. Hang, Homology and orientation reversing periodic maps on surfaces, Topology and its Applications, 229 (2017), 1-19. [doi] [arXiv]