Optimal transport and machine learning
Scott Mahan, Caroline Moosmüller, Alex Cloninger. “Point Cloud Classification via Deep Set Linearized Optimal Transport”, arXiv preprint: 2401.01460, 2024.
Keaton Hamm, Caroline Moosmüller, Bernhard Schmitzer, Matthew Thorpe. “Manifold learning in Wasserstein space”, arXiv preprint: 2311.08549, 2023.
Shiying Li, Caroline Moosmüller. “Approximation properties of slice-matching operators”, arXiv preprint: 2310.10869, 2023.
Shiying Li, Caroline Moosmüller. “Measure transfer via stochastic slicing and matching”, arXiv preprint: 2307.05705, 2023.
Alex Cloninger, Keaton Hamm, Varun Khurana, Caroline Moosmüller. “Linearized Wasserstein dimensionality reduction with approximation guarantees”, arXiv preprint: 2302.07373, 2023.
Varun Khurana, Harish Kannan, Alex Cloninger and Caroline Moosmüller. “Supervised learning of sheared distributions using linearized optimal transport”, Sampling Theory, Signal Processing, and Data Analysis, Volume 21, Article number 1, 2023
Caroline Moosmüller, Alex Cloninger. “Linear optimal transport embedding: provable Wasserstein classification for certain rigid transformations and perturbations”, Information and Inference: A Journal of the IMA, Volume 12, Issue 1, pp. 363–389, 2023
Caroline Moosmüller, Felix Dietrich, Ioannis G. Kevrekidis. “A geometric approach to the transport of discontinuous densities”, SIAM/ASA J. Uncertainty Quantification, vol. 8 (3), pp. 1012-1035, 2020
Applications in biology and cancer research
A. P. Tran, C. Tralie, J. Reyes, C. Moosmüller, Z. Belkhatir, I. G. Kevrekidis, A. J. Levine, J. O. Deasy, A. Tannenbaum, “Long-term p21 and p53 dynamics regulate the frequency of mitosis and cell cycle arrest following radiation damage”, Cell Death & Differentiation 30, pp. 660-672, 2023
M. Pouryahya, J. H. Oh, J. C. Mathews, Z. Belkhatir, C. Moosmüller, J. O. Deasy, A. R. Tannenbaum, “Pan-Cancer Prediction of Cell-Line Drug Sensitivity Using Network-Based Methods”, International Journal of Molecular Sciences 23(3), article number: 1074, 2022
C. Moosmüller, C. J. Tralie, M. Kooshkbaghi, Z. Belkhatir, M. Pouryahya, J. Reyes, J. O. Deasy, A. R. Tannenbaum, I. G. Kevrekidis, “Periodicity scoring of time series encodes dynamical behavior of the tumor suppressor p53”, IFAC-PapersOnLine, vol. 54 (9), pp. 488-495, 2021
J. Mathews, M. Pouryahya, C. Moosmüller, I. G. Kevrekidis, J. Deasy, A. Tannenbaum, “Molecular phenotyping using networks, diffusion, and topology: soft-tissue sarcoma”, Scientific reports 9, Article number: 13982, 2019
Approximation theory
M. Cotronei, C. Moosmüller, T. Sauer, N. Sissouno, “Hermite multiwavelets for manifold-valued data”, Advances in Computational Mathematics, vol. 49, article number: 40, 2023
M. Cotronei, C. Moosmüller, “Hermite B-splines: n-refinability and mask factorization”, Mathematics, vol. 9 (19), article number: 2458, 2021
C. Moosmüller, T. Sauer, “Factorization of Hermite subdivision operators from polynomial over-reproduction”, Journal of Approximation Theory, vol. 271, article number: 105645, 2021
C. Moosmüller, S. Hüning, C. Conti, “Stirling numbers and Gregory coefficients for the factorization of Hermite subdivision operators”, IMA Journal of Numerical Analysis 41(4), pp. 2936-2961, 2021
M. Cotronei, C. Moosmüller, T. Sauer, N. Sissouno, “Level-dependent interpolatory Hermite subdivision schemes and wavelets”, Constructive Approximation, vol. 50 (2), pp. 341 – 366, 2019
C. Moosmüller, N. Dyn, “Increasing the smoothness of vector and Hermite subdivisions schemes”, IMA Journal of Numerical Analysis, vol. 39 (2), pp. 579 – 606, 2019
C. Moosmüller, “A note on spectral properties of Hermite subdivision operators”, Computer Aided Geometric Design, vol. 69, pp. 1 -10, 2019
C. Moosmüller, “Hermite subdivision schemes on manifolds via parallel transport”, Advances in Computational Mathematics, vol. 43 (5), pp. 1059 – 1074, 2017
C. Moosmüller, “C1 analysis of Hermite subdivision schemes on manifolds”, SIAM Journal on Numerical Analysis, vol. 54 (5), pp. 3003 – 3031, 2016