Projects & Funding

The Networks, Computation, and Social Dynamics (NCASD) Lab studies the structure and evolution of complex systems, from the architecture of proteins to the dynamics of organizational collaboration.  We develop and apply mathematical, computational, and statistical techniques for the modeling and measurement of structure, function, and dynamics, with a particular focus on social, biological, and physical systems that can be characterized in terms of relational (i.e., network) structure.  Housed within the Center for Networks and Relational Analysis at the California Institute for Telecommunications and Information Technology (Calit2), the NCASD Lab is an interdisciplinary group whose members variously hail from the social, computational, statistical, and physical sciences and engineering.  Our work seeks to make basic science contributions that also inform technology, policy, and human health.

Social systems are characterized by complex relational structure, that shapes and is shaped by both the microdynamics of individual behavior and the physical, cultural, and institutional contexts in which they are embedded.  A major thrust of our research is on understanding the mechanisms that drive the formation and evolution of social structure and the dynamics of social interaction at the individual, group, and organizational levels.  We also examine the effect of social context on decision making, information sharing, perception, and belief.  A recurring theme in our work is the effect of disruption on social systems, the ways in which groups and organizations respond to such disruptions, and how to facilitate effective response.  This includes disruptions associated with emergencies and disasters arising from natural or anthropogenic hazards, and organizations’ strategies for combating them.

Computational and statistical methods are the backbone of our work, enabling us to both probe the behavior of complex systems and infer their characteristics (often from fragmentary and error-prone observational data).  An important thrust of our work thus involves novel applications of simulation techniques, graph algorithms, machine learning, and computational and Bayesian statistical methods to social, biological, physical, and technical systems with complex structure and dynamics.  We also, however, develop novel methodology for both computing and data analysis, including algorithms for stochastic simulation as well as techniques for measurement and analysis of relational data.  A major theme of our work in this area is the development of models for relational systems that capture dependence in a mechanistically realistic way, while still being statistically tractable (and usable with actually existing data).  We also work on problems of measurement, including sampling designs for dynamic and relational data, models for inference from data sources with complex error structure (ranging from physical instruments to human informants), and novel ways of leveraging spectral or other features to infer latent behavior.

From the very large to the very small, biological systems are characterized by complex patterns of interdependencies that are essential for survival and function.  A major thrust of our research is on studying the emergence and persistence of complex structure in biological systems at the molecular level, and in identifying novel approaches to characterize variation in molecular structure and dynamics that are of potential functional relevance.  This includes not only the emergence of structures that are biologically functional (e.g., enzymatic complexes, lipid bilayers), but also those that are pathological (e.g., the amyloid fibrils that lead to Lewy body dementia, or the unstructured protein aggregates responsible for cataract).  Much of our work leverages the adaptation of modeling approaches originally developed for social systems to structure and interaction among molecules.  We also work on comparative modeling and analysis of protein ensembles, with the goal of identifying enzymes with novel function (e.g., in the context of enzyme discovery), detecting evolutionary trends (e.g., in response to extreme environments, or pathogen response to a novel host), or characterizing the relationship between sequence variation and function (e.g., for protein engineering).