معرفی
Hector McKimm is a Researcher in the Department of Statistics at Imperial College London, where he collaborates with the California-Harvard Astrostatistics Collaboration (CHASC) to apply Bayesian statistics to astronomical data. His work focuses on advancing computational methods like Monte Carlo techniques and Markov processes for astrostatistical analysis.
He completed his PhD at the University of Warwick under the Oxford-Warwick Statistics Programme (OxWaSP), with a thesis on 'Monte Carlo Methods based on Novel Classes of Regeneration-enriched Markov processes.' Prior to his PhD, he studied Mathematics at Durham University as part of St Mary's College.
McKimm's research interests span Bayesian methodology, astrostatistics, and computational statistics. He has contributed to pandemic modeling during the COVID-19 crisis, co-authoring influential papers on school reopening impacts and UK exit strategies. His recent conference activities include presentations on Hamiltonian Jump Processes and Adaptive Simulation techniques at events such as the CIRM Bayesian Statistics conference.
He has been involved with The Alan Turing Institute through its Engage@Turing program and contributed to a data analytics project on weather's impact on ASDA sales. Teaching experience includes tutoring first-year Probability modules at Warwick.
