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.

The question underneath all of it has stayed roughly the same: how local information, relational structure, and individual inference combine to produce collective outcomes. The settings have changed — small experiments, organizations, platforms, and now networks of machine agents — but that is the problem each of them was a way of getting at.

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, from the substantive question through to which conclusions the design can actually support. The method follows the inferential problem rather than defining it in advance; the specific methods and technical capacities are inventoried on Skills & Capacities.

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

The same question now runs through networks of machine agents: what communication structure does to collective judgment, what happens to information as it moves, and what a hidden process leaves recoverable. The research programme sets out the individual studies, and the systems built to run them.

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.