Network Dynamics
Many important systems can be modeled as complex networks in which the state of each node is represented by a dynamical equation. Each node is influenced to some extent by its neighbors in the network. While these models are intuitively appealing, they can easily become impracticably complex. In Naoki Masuda’s lab we are working on ways to better approximate — and therefore make more manageable — the current system state, as well as to predict the future state of the system more efficiently and effectively.
Cooperative and Competitive Behavior in Small Groups
It is remarkable how readily humans work together, yet our interactions in groups are often inefficient and occasionally counterproductive. Why? I investigate communication features such as the timing, duration, pitch, and volume of speech, that may relate to social structure, relationship quality, and other group characteristics. I use network and other complex systems models to understand patterns in interactions between individuals in work groups.
Social Networks
Humans differ in important ways across regions and cultures, and we expect relationships to be structured differently as well. Social network analysis has the power to highlight informative patterns in these differences. However, relationship data can be costly to obtain, so it is scarce and rarely replicated, and — strangely — the apparently highly non-random nature of empirical networks makes comparison between networks difficult. I adapt existing tools, such as exponential random graph modeling and ideas from spatial network analysis, to better understand social networks within and between societies.
Data Analysis
I have collaborated with teams working on many interesting problems. My work searches for creative solutions to the many challenges of working with data from human participants. One of my specialties is finding simple visual representations that can clarify perplexing results and communicate lessons from large, complicated data sets.
