Regular biography
Yi Fan is a distinguished researcher with a strong background in statistics, machine learning, and computational methods. He is currently affiliated with the University of New South Wales (UNSW) and has made significant contributions to the fields of Approximate Bayesian Computation (ABC), probabilistic modeling, and data-driven analysis. His work often bridges theoretical statistics with practical applications in areas such as climate science, medical imaging, and machine learning. Yi has published extensively in top-tier journals and conferences, including *Nature Communications*, *PLOS ONE*, *IEEE Transactions*, and *Proceedings of Machine Learning Research (PMLR)*. His research interests include Bayesian inference, likelihood-free methods, probabilistic graphical models, and their applications in complex systems. He is also actively involved in interdisciplinary collaborations, particularly in the analysis of climate data and the development of advanced statistical tools for scientific inference.