About

I am a behavioral and computational scientist working across social networks, organizational behavior, computational social science, and AI-mediated systems. My research examines how information and relationships shape what people and machines can know, how they respond to one another, and what happens when those local processes become properties of a larger system.

Background

I trained in sociology and organizational behavior, earning an MA in Sociology from the University of South Carolina and a PhD in Organizational Behavior from Cornell University. My early research examined social networks, diffusion, error, social perception, status, cooperation, negotiation, and collective behavior.

Across those projects, a recurring problem was partial information: people infer qualities of other actors and environments from incomplete cues, receive information through relationships, and make judgments that alter subsequent interaction.

I later served as an Assistant Professor of Management and Organizations at the University of Kentucky’s LINKS Center for Social Network Analysis and as a Robert K. Merton Visiting Research Fellow at the Institute for Analytical Sociology in Stockholm. My academic work combined controlled experiments, social-network analysis, computational modeling, and behavioral theory to study how communication and network structure generate individual and collective outcomes.

A substantial part of my career has also been spent in applied research. At Meta, Roku, and Iron Light, I worked on behavioral measurement, experimentation, network and computational analysis, and research infrastructure. Those settings introduced different scales, data sources, operational constraints, and decision contexts, but many of the underlying scientific problems remained continuous with my academic work.

Research practice

I tend to work across the full research process: defining the substantive question, specifying plausible mechanisms, deciding what must be measured or manipulated, building the necessary experimental or computational system, analyzing and validating the resulting evidence, and determining which conclusions the design can support. The method follows the inferential problem rather than defining it in advance.

Building research infrastructure became a recurring part of my work because some questions required constructing the environment, measurement system, or analytical process through which they could be studied.

During graduate school, I managed operations for Cornell’s Business Simulation Laboratory and helped establish the Cornell Sociology Social Science Research Laboratory. More recently, that has included experimental and simulation environments, analytical and measurement systems, research applications, and standalone research software.

Current work

My current research focuses especially on distributed information processing, communication structure, collective judgment, information quality, inference from incomplete traces, diffusion, adaptation, and networked AI systems.

Much of my current work uses artificial or simulated systems because their underlying structure can be specified and observed directly. That makes it possible to test candidate mechanisms and measurements against known conditions before asking whether the same relationships hold in human, organizational, or less completely observed systems.