Nilabja Guha

Nilabja Guha is a Lecturer in Probability and Statistics at The University of Manchester, Department of Mathematics. His research interests include Bayesian modeling of high-dimensional problems, change-point-related problems, probabilistic graphical models, Bayesian modeling of inverse problems and uncertainty quantification, and stochastic approximation-based estimation methods. He is currently accepting PhD students. His academic qualifications include a Master of Statistics from the Indian Statistical Institute and a Doctor of Philosophy from the University of Maryland Baltimore County (UMBC).

Nilabja Guha is a Lecturer in Probability and Statistics at The University of Manchester, specializing in Bayesian Modeling, Inverse Problem, Uncertainty Quantification, and Non-parametric Statistics. His research focuses on developing Bayesian and variational Bayesian methods for solving inverse problems, particularly in heterogeneous media and complex statistical models. He explores nonparametric Bayesian approaches for benchmark dose estimation, quantile graphical models, and dynamic data-driven Bayesian methods. His work also includes uncertainty quantification for differential equations and Bayesian survival models using latent Gaussian processes. Guha's research emphasizes probabilistic modeling of missing information and stochastic algorithms for robust statistical inference.