Munk, Axel

Authors Requardt R, Fink M, Kubica P, Steinem C, Munk A, Li H   Journal IEEE Transactions on NanoBioScience (TNB)   Citation IEEE Transactions on NanoBioScience (TNB). 2025.   Abstract Recent experimental studies have shed light on the intriguing possibility that ion channels exhibit cooperative behaviour. However, a comprehensive understanding

Authors Blob A, Ventzke D, Rölleke U, Nies G, Munk A, Schaedel L, Köster S   Journal Soft Matter   Citation Soft Matter, 2025.   Abstract The eukaryotic cytoskeleton is an intricate network of three types of mechanically distinct biopolymers — actin filaments, microtubules and intermediate filaments (IFs). These filamentous

Authors Hundrieser S, Klatt M, Munk A, Staudt T Journal Bernoulli Citation Bernoulli 30 (4), 2846-2877. Abstract We provide a unifying approach to central limit type theorems for empirical optimal transport (OT). The limit distribution is given by a supremum of a centered Gaussian process, and we explicitly characterize when it

Authors Gonualez-Sanz A, Groppe M, Munk A Journal Arxiv Citation arXiv:2410.23146. Abstract The goal of optimal transport (OT) is to find optimal assignments or matchings between data sets which minimize the total cost for a given cost function. However, sometimes the cost function is unknown but we have access to

Authors König C, Munk A, Werner F Journal Journal of the Royal Statistical Society Series B: Statistical Methodology Citation J R Stat Soc Series B: Statistical Methodology, 2024, 00, 1–19. Abstract We develop a multiscale scanning method to find anomalies in a d-dimensional random field in the presence of nuisance

Authors Naas J, Nies G, Li H, Stoldt S, Schmitzer B, Jakobs S, Munk A Journal Communications Biology Citation Commun Biol 7, 1139 (2024). Abstract With recent advances in multi-color super-resolution light microscopy, it is possible to simultaneously visualize multiple subunits within biological structures at nanometer resolution. To optimally evaluate

Authors Kovacs S, Li H, Haubner L, Munk A, Buhlmann P Journal Journal of Machine Learning Research Citation J Mach Learn Res 25(297):1−64, 2024. Abstract Change point estimation is often formulated as a search for the maximum of a gain function describing improved fits when segmenting the data. Searching one

Authors Hundrieser S, Mordant G. Weitkamp CA, Munk A Journal Stochastic Processes and their Applications Citation Stochastic Processes and their Applications 178 (2024) 104462. Abstract Optimal transport (OT) based data analysis is often faced with the issue that the underlying cost function is (partially) unknown. This is addressed in this

Authors Suchan L, Li H, Munk A Journal Arxiv Citation arXiv:2407.15297. Abstract Graph cuts are among the most prominent tools for clustering and classification analysis. While intensively studied from geometric and algorithmic perspectives, graph cut-based statistical inference still remains elusive to a certain extent. Distributional limits are fundamental in understanding

Authors Vanegas LJ, Eltzner B, Rudolf D, Dura M, Lehnart SE, Munk A Journal The Annals of Applied Statistics Citation Ann. Appl. Stat. 18(2): 1445-1470. Abstract We propose and investigate a hidden Markov model (HMM) for the analysis of dependent, aggregated, superimposed two-state signal recordings. A major motivation for this