Professor Ziheng Yang is a world-renowned scientific leader whose pioneering work has fundamentally transformed the field of statistical genetics and evolutionary biology. He currently holds the prestigious R.A. Fisher Chair of Statistical Genetics at University College London and serves as Director of the R.A. Fisher Centre for Computational Biology. After earning his PhD in agronomy from Beijing Agricultural University in 1992, he pursued postdoctoral research at the University of Cambridge, the Natural History Museum in London, Pennsylvania State University, and the University of California at Berkeley. He joined University College London in 1997 as a Lecturer, advancing to Reader in 2000, Professor in 2001, and ultimately securing the distinguished R.A. Fisher Chair in 2010, establishing himself as a preeminent figure in computational biology.
Professor Yang has developed groundbreaking statistical methods and computational algorithms that have revolutionized comparative analysis of genetic sequence data, enabling evolutionary biologists to extract vast historical information from modern genomes. His widely used software packages, including PAML and BPP, provide essential tools for molecular evolution studies, species delineation, and population demography analysis across the biological sciences. His influential book Molecular Evolution: A Statistical Approach (2014) offers a comprehensive synthesis of methodological developments in evolutionary genomics and has become a standard reference in the field. These contributions have rejuvenated molecular systematics and enabled researchers to identify genetic changes responsible for major evolutionary adaptations by working both forwards and backwards in time.
Elected a Fellow of the Royal Society in 2006, Dr. Yang has received numerous prestigious honors including the Society of Systematic Biologists' Presidents' Award for Lifetime Achievement in 2008 and the Zoological Society of London's Frink Medal in 2010. His research continues to advance Bayesian statistical inference methods under the coalescent model, with recent work focused on improving computational efficiency for analyzing large genome-scale datasets. As principal investigator of the Yang Lab at UCL, he mentors numerous researchers and maintains an active program developing new computational approaches in evolutionary genomics. His ongoing contributions ensure that statistical genetics remains at the forefront of biological discovery as genomic data continues to expand exponentially.
