My research interests are organized around four main areas:

🌱 Socio-ecological systems dynamics
I study the interactions between human societies and ecosystems, with particular attention to the social, economic, and political processes that shape these dynamics.

💚 Nature’s values
I explore the diverse ways in which people value nature and work on integrating these perspectives into decision-making processes and public policy design.

🦋 Biodiversity management and conservation
I apply computer vision, statistical modeling, and spatial analysis to species classification and monitoring, generating evidence to support biodiversity management and conservation strategies.

🏛️ Governance and public policy
I analyze policy integration and coherence using quantitative and data-driven methods, aiming to improve policy design, implementation, and decision-making processes.

Overall, my research seeks to integrate interdisciplinary approaches to strengthen sustainability, generating knowledge and innovative tools based on data science and artificial intelligence that support both public policy development and the management and conservation of socio-ecological systems.

Social network analysis has an especially long tradition in the social science. In recent years, a dramatically increased visibility of SNA, however, is owed to statistical physicists. Among many, Barabasi-Albert model (BA model) has attracted particular attention because of its mathematical properties (i.e., obeying power-law distribution) and its appearance in a diverse range of social phenomena. BA model assumes that nodes with more links (i.e., “popular nodes”) are more likely to be connected when new nodes entered a system. However, significant deviations from BA model have been reported in many social networks. Although numerous variants of BA model are developed, they still share the key assumption that nodes with more links were more likely to be connected. I think this line of research is problematic since it assumes all nodes possess the same preference and overlooks the potential impacts of agent heterogeneity on network formation. When joining a real social network, people are not only driven by instrumental calculation of connecting with the popular, but also motivated by intrinsic affection of joining the like. The impact of this mixed preferential attachment is particularly consequential on formation of social networks. I propose an integrative agent-based model of heterogeneous attachment encompassing both instrumental calculation and intrinsic similarity. Particularly, it emphasizes the way in which agent heterogeneity affects social network formation. This integrative approach can strongly advance our understanding about the formation of various networks.