Dr Peng Wang is a network methodologist who specializes in the development of statistical models for social network analysis. With a combination of skills in computer science, mathematics and statistics, and social network theory and analysis, Dr Wang has detailed understanding of the advantages of exponential random graph models (ERGMs) for social networks, as well as the challenges that need to be overcome. Collaborating with world renowned leaders in the field of social network analysis, Dr Wang personally contributed to the advance of ERGMs in model specifications, methods for simulations and estimations techniques, computational efficiency and model robustness, and model interpretations and empirical implications. Dr Wang developed the PNet software package for the simulation and estimation of ERGMs. The PNet software serves as an essential part of the SNA research team in Melbourne – MelNet, as well as the general SNA community. Dr Peng Wang’s work contribute to the development of ERGMs and PNet into cases of bipartite, multivariate, longitudinal, nodal attribute based and multilevel network models, with methodological developments on model specifications, conditional estimations on snowball sampled network data, models with missing network data, and models for large networks. He has publications in the fields of Management, Social Ecological Systems, Networks among Adolescents, Disease Transmission and Public Health Issues, Research Collaboration Networks, Political Networks and Interlocking Directorates networks. Dr Wang is currently working at the Centre for Transformative Innovation (CTI), Swinburne University of Technology, focuses on the development of a new statistical framework for the co- evolution of network structure and nodal attributes, and the application of such methods.