Simon Cotter

Simon Cotter is a Professor in Applied Mathematics at The University of Manchester, Department of Mathematics. His research interests include Bayesian methods, optimization, and computational statistics, with contributions to areas such as Markov Chain Monte Carlo algorithms and hierarchical Bayesian data selection. His work aligns with the United Nations Sustainable Development Goals, particularly in promoting gender equality and reducing inequalities. Cotter's research has been published in several high-impact journals, including SIAM/ASA Journal on Uncertainty Quantification and ACM Transactions on Probabilistic Machine Learning. His profile page provides further details on his research and publications.

Simon Cotter is a researcher in the School of Mathematics at the University of Manchester, specializing in Bayesian inverse problems and stochastic chemical kinetics. His work focuses on developing and applying probabilistic methods to solve inverse problems in mathematical models, particularly in fluid mechanics and PDEs. He also investigates stochastic simulation techniques for chemically reacting systems, including multiscale models and their approximation. Cotter's research includes Bayesian data assimilation, variational methods, and numerical algorithms for efficient sampling and inference. His publications address challenges in modeling complex systems with uncertainty, emphasizing robust and computationally feasible approaches.