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Nick Tawn is a Reader in Statistics at the University of Warwick, where his research interests primarily focus on developing scalable Monte Carlo methodologies in complex Bayesian settings. He has a particular interest in Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) techniques. His work explores MCMC methodology in scenarios where the target distribution exhibits multi-modality. Nick completed his PhD in 2017, which was titled 'Optimality Parallel Tempering Algorithm.' Following the completion of his thesis, he continued to engage in similar research problems while working on related publications. Outside of academia, he is an avid cyclist, swimmer, and runner, drawing inspiration for his work from his multi-modal adventures.
Includes General, Mechanical, Civil, Electrical, Biomedical, and Manufacturing Engineering. Most programs fall under English Band A.