Ed Cohen is a lecturer in the Statistics Section of the Department of Mathematics. His research interests lie broadly in the areas of statistical signal and image processing with focus on: multivariate time series, multivariate point processes, wavelet analysis of time series, wavelet analysis of point processes, complex valued time series and nonstationarity.
Motivated by applications in the natural sciences his recent work focuses on applications in fluorescence microscopy with ongoing collaboration including the Ward-Ober lab at Texas A&M, the Experimental Biophysics & Nanotechnology group at King's College London and the Quantitative and Nanobiophysics Lab at UCL.
Ed currently teaches M345S08 Time Series, and has previously taught M34S7 Statistical Pattern Recognition, M5MS07 Nonparametric Smoothing and Wavelets and M5MS08 Multivariate Analysis
et al., 2017, 3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse, Scientific Reports, Vol:7
Hoagn J, Cohen E, Adams N, 2017, Devising a fairer method for adjusting target scores in interrupted one-day international cricket, Electronic Journal of Applied Statistical Analysis, Vol:10, ISSN:2070-5948, Pages:745-758
Cohen E, Kim D, Ober RJ, 2015, The Cramer Rao lower bound for point based image registration with heteroscedastic error model for application in single molecule microscopy, Ieee Transactions on Medical Imaging, Vol:34, ISSN:1558-254X, Pages:2632-2644
Gibberd A, Nobel J, Cohen E, 2018, Characterising dependency in computer networks using spectral coherence, International Conference on Time Series and Forecasting, ITISE
Taleb Y, Cohen E, 2016, A wavelet based likelihood ratio test for the homogeneity of poisson processes, 2016 IEEE Statistical Signal Processing Workshop (SSP), IEEE