About Me
I am an incoming second-year PhD student in Arizona State University's Department of Psychology working under Dr. Athena Aktipis. I study how information is transmitted, filtered, and collectively processed in complex networked systems, with a particular focus on what happens when those systems are placed under pressure.
During my undergraduate education I studied the dynamics between individuals during emergency evacuations, which first drew my attention to how collective behavior is shaped by the structure of the networks through which information moves. I pursued this further after graduating, spending a year working with the Federal Emergency Management Agency on disaster preparation and response through the AmeriCorps FEMA Corps program. After many years leading production teams in the manufacturing sector, I've returned to academia to take these questions somewhere I find them most consequential: the design of information networks in high-stakes environments where the cost of failure is high and the margin for error is small.
Research
Current Focus
Some of the most remarkable information-processing systems ever designed were not computers. They were human organizations, built specifically to allow groups of people to perceive, interpret, and act on complex, rapidly changing situations that no individual could fully comprehend alone. What these systems share, across contexts as different as military command and control, civilian air traffic management, and crewed spaceflight operations, is a deliberate architecture: a set of decisions about who receives what information, when, in what form, and with what authority to act on it. My research looks at these systems as objects of scientific inquiry. It asks what network-level properties actually account for their effectiveness under pressure, and how those properties are changed by the technologies used within the systems. I apply network science, information theory, and the study of human/autonomy teaming to understand how information flows through mixed human/machine networks, and how those networks effectively detect, process, and respond to anomalies.
Ongoing Work
- The development of benchmark metrics to measure large language models' understanding of the biological principles of division of labor and its ability to use them effectively in its interactions with users. This is part of a broader development process within the Cooperative Futures Institute to design and make available benchmarks to quantify models' capacities to engage in cooperative behavior.
- The implementation of an interface for active, real-time musical improvisation between human and generative sound models
- *Bespoke graduate research agenda based off Current Focus loading...*