Ed Cohen is a Senior Lecturer in the Statistics Section of the Department of Mathematics. His research interests lie broadly in the areas of statistical signal and image processing. Particular areas of focus include: the development of wavelet and spectral methodology for the analysis of multivariate point processes, change point detection, adaptive estimation, and self-exciting processes - including issues of identifiability and estimation when event data is aggregated. 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 the Ward-Ober lab at University of Southampton, the Experimental Biophysics & Nanotechnology group at University of Birmingham, the Quantitative and Nanobiophysics Lab at UCL, and the Institute of Immunity and Transplantation at UCL.
Ed currently teaches M345S08 Time Series Analysis and M5MS08 Multivariate Analysis, and has previously taught M34S7 Statistical Pattern Recognition, and M5MS07 Nonparametric Smoothing and Wavelets.
Cohen E, Gibberd A, Wavelet spectra for multivariate point processes, Biometrika, ISSN:0006-3444
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 SIM using deep learning, Royal Society of London. Philosophical Transactions A. Mathematical, Physical and Engineering Sciences, ISSN:1364-503X
Taleb Y, Cohen E, 2021, Multiresolution analysis of point processes and statistical thresholding for Haar wavelet-based intensity estimation, Annals of the Institute of Statistical Mathematics, Vol:73, ISSN:0020-3157, Pages:395-423
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