Ed Cohen is a Reader in Statistics in the Department of Mathematics. His research interests lie broadly in statistical signal and image processing. Particular areas of focus include: the analysis of multivariate event and count processes, network processes, adaptive estimation and change point detection. He also develops methodology for the quantitative analysis of bioimaging data with interests including spatial statistics, clustering, molecular counting and metrology.
Motivated by applications in engineering and the natural sciences, his collaborators include a diverse set of academic and industry collaborators, from the life sciences to cyber security.
Ed is an investigator and a project lead on the EPSRC Network Stochastic Processes and Time Series (NeST) Programme.
et al., 2023, Nonparametric estimation of the intensity function of a spatial point process on a Riemannian manifold., Biometrika, ISSN:0006-3444
et al., 2023, A framework for evaluating the performance of SMLM cluster analysis algorithms, Nature Methods, ISSN:1548-7091
Cohen E, Gibberd A, 2022, Wavelet spectra for multivariate point processes, Biometrika, Vol:109, ISSN:0006-3444, Pages:837-851
Shlomovich L, Cohen E, Adams N, A parameter estimation method for multivariate binned Hawkes processes, Statistics and Computing, ISSN:0960-3174
et al., 2022, Parameter estimation of binned Hawkes processes, Journal of Computational and Graphical Statistics, Vol:31, ISSN:1061-8600, Pages:990-1000
et al., 2021, Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM), Bioinformatics, Vol:37, ISSN:1367-4803, Pages:2730-2737
et al., 2021, Improving axial resolution in Structured Illumination Microscopy using deep learning, Royal Society of London. Philosophical Transactions A. Mathematical, Physical and Engineering Sciences, Vol:379, ISSN:1364-503X
Ward S, Cohen E, Adams N, 2021, Testing for complete spatial randomness on three dimensional bounded convex shapes, Spatial Statistics, Vol:41, ISSN:2211-6753
et al., 2019, A hidden Markov model approach to characterizing the photo-switching behavior of fluorophores, Annals of Applied Statistics, Vol:13, ISSN:1932-6157, Pages:1397-1429
et al., 2019, Resolution limit of image analysis algorithms, Nature Communications, Vol:10, ISSN:2041-1723