Research
These themes are not mutually exclusive folders — a single project often belongs to more than one. Each theme page links to the concrete projects, publications, and software built under it.
What mechanisms are capable of generating the behavior we observe?
I study inverse problems in generative systems: when observations are produced by complex models, what can we infer about the rules or mechanisms that generated them? My current work focuses on equifinality, rule identification, and uncertainty-aware inference for agent-based models.
How do interactions among individuals produce diffusion, influence, and collective dynamics?
I study how information and behavior propagate through interacting populations, how influence can be represented and measured, and how local behavioral rules produce large-scale social dynamics.
How can machine learning, simulation, and scalable computation help us model and understand complex adaptive systems?
I develop and use computational models that combine simulation, machine learning, and scalable implementation to study systems whose collective behavior emerges from many interacting components.
How can complex longitudinal, categorical, relational, and behavioral data be represented and analyzed statistically?
I work on statistical representations and methods for complex social and behavioral data, including categorical longitudinal trajectories, network structure, and compositional similarity.
Projects
Many behavioral processes are observed as categorical trajectories rather than ordinary numeric time series. This work develops methods for representing and clustering densely observed categorical longitudinal data and applies them to social-media behavior.
Read the project ↗SocialSim investigated computational approaches to modeling and forecasting online social behavior. My work centered on a multi-platform simulation framework and the Multi-Action Cascade Model, connecting behavioral assumptions about influence with scalable implementation.
Read the project ↗Current-research flagship
Different generative mechanisms can produce similar observable behavior. This work investigates how machine learning and conformal prediction can be used to identify sets of plausible behavioral rules in agent-based models rather than forcing a single overconfident explanation.
Read the project ↗This line of work studies how information spreads through social systems and how influence can be measured across actors and communities. It spans my doctoral research, diffusion models, and DARPA MIPS work on influence pathways using transfer entropy and network-based methods.
Read the project ↗