Mark Stephen Handcock is an Australian-American statistician. He is Distinguished Professor of Statistics at the University of California, Los Angeles. and also the Dunn Family Endowed Chair of Data Theory. His research focuses on statistical problems motivated by the social sciences and demography, particularly statistical models for social networks, including exponential-family random graph models (ERGMs) and latent space models, as well as demography, spatial statistics, social epidemiology, and environmental statistics. He is a Fellow of the American Statistical Association.
Handcock has made foundational contributions to the statistical specification and inference of exponential-family random graph models (ERGMs). This framework has been widely adopted across disciplines for modeling complex dependencies in social networks.
In collaboration with Peter D. Hoff and Adrian E. Raftery, Handcock developed latent space approaches for social network analysis. This methodology addresses transitivity and homophily by embedding network actors in a latent metric space, a technique that has become a standard fixture in both statistical modeling and machine learning applications.
Handcock pioneered relative distribution methods, motivated by social science questions. This nonparametric statistical framework isolates changes in the shape of a distribution from shifts in its median, providing researchers with tools to analyze socioeconomic polarization and income inequality.
Handcock is a founding developer of the `statnet` suite of packages in R, which provides a comprehensive computational framework for the representation, visualization, and analysis of network data. The software suite is extensively utilized in applied network analysis and is featured in standard computational statistics curricula.
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