Research Statement

My research develops computational systems to study cognition as a process of regulation rather than optimization. The prototypes I design are not standalone tools, but experimental platforms grounded in a shared theoretical commitment: that intelligent systems—biological or artificial—must continually maintain viable interaction with changing environments. Across my work in Enactive Perception, Enactive Regulation Theory (ERT), and Enactive Drift Regulation (EDR), I argue that meaning does not arise from static representations or isolated computations. Instead, it emerges through ongoing interaction, adaptation, stabilization, and reorganization over time. These systems operationalize this view by treating perception, creativity, learning, and coordination as dynamic processes that must remain coherent under conditions of drift.

Traditional AI systems typically aim to optimize performance toward fixed objectives. However, real-world cognition rarely operates in stable conditions. Humans do not solve problems once and deploy solutions indefinitely; we adjust to shifting contexts, change strategies, balance exploration and consolidation, and sustain coherence as goals and environments evolve. The prototypes I develop translate this insight into computational form by framing intelligence as the capacity to maintain meaningful coupling with the world over time.

Central to this work is the development of an enactive model of creativity, which treats creative activity not as the generation of novel artifacts but as the ongoing regulation of exploratory and stabilizing processes. Creativity unfolds through interaction between an agent and its environment, involving cycles of variation, consolidation, and reorganization. Similarly, my enactive model of consciousness describes awareness as emerging from the regulation of sense-making across time—where agents dynamically shift between prediction-driven engagement and exploratory openness. These shifts can be formally represented as sense-making curves, which capture how cognitive systems move between stability and adaptability as they negotiate changing conditions.

To study these dynamics empirically, I develop computational frameworks for co-creative sense-making that enable quantification of human–AI interaction. Rather than focusing solely on outcomes, these models measure how meaning emerges through interaction—tracking patterns such as tempo, variability, structural emergence, and coherence across time. This allows creativity and collaboration to be analyzed as temporally extended processes rather than static achievements.

Across diverse domains—including drawing, art therapy, financial time-series, physiological signals, and multi-agent interaction—these systems share a common architectural principle: they operate online, detect change, adapt in real time, and regulate their behavior rather than producing outputs in isolation. Some focus on regulating learning under shifting conditions, as in the Emergence Machine; others explore the regulation of human–AI creative interaction, such as Aether, the AI Drawing Partner, and the Participatory Stroke Engine. The Quantified Art Therapy Interface makes regulatory dynamics measurable during expressive activity, while the Multi-Agent Drawing Environment examines how coherence emerges collectively through distributed participation.

Taken together, these platforms explore a unifying claim: intelligence is the ability to remain viable in a changing world. This requires maintaining structure without rigidity, allowing novelty without fragmentation, and shifting modes of operation without collapse. Each system examines these principles from a different vantage point—learning, creativity, therapeutic expression, or collective coordination—serving not only as practical applications but as research instruments. By grounding AI in interaction rather than output, this work advances a new direction for artificial intelligence: systems that do not simply solve predefined problems, but remain coherent as the problems themselves evolve.