Ph.D. in Human-Centered Computing from Georgia Tech

B.A. in Cognitive Science from Case Western Reserve University

I received my PhD in Human-Centered Computing from Georgia Tech in 2017, specializing in Cognitive Science and Computational Creativity. I served as an Assistant Professor in Human–Computer Interaction (HCI) in the Department of Software and Information Systems at the University of North Carolina at Charlotte for four years, where I taught human-centered design and co-creative AI while conducting research on the design and evaluation of human–AI creative systems.

During my doctoral training, I spent five years as a Graduate Research Assistant in Georgia Tech’s Expressive Machinery Lab, where I developed and empirically evaluated co-creative AI systems to better understand creative cognition in human–AI interaction. I also conducted research in the Entertainment Intelligence Lab exploring AI for education, and in the ACME Lab investigating sketch-based tools for early dementia screening.

In addition to academic research, I have worked as a User Experience Research Intern at Google, YouTube’s Visioning Team, and Adobe’s Creative Technologies Lab.

I currently work as an independent research consultant studying and designing interactive AI systems.

My research focuses on understanding how humans and AI systems collaborate in real time — not simply as tool users and tools, but as dynamically coupled partners in shared sense-making.

While co-creative AI has traditionally emphasized outcomes (e.g., generated artifacts), my work investigates the interaction dynamics that unfold during collaboration itself.

I study how:

• meaning emerges through interaction
• creative direction stabilizes or destabilizes over time
• human–AI systems maintain coherence under changing conditions

To do this, I develop computational and cognitive models of interaction grounded in enactive and dynamical systems approaches to cognition.

This work has led to the development of a framework for modeling creative interaction as a process of ongoing regulation rather than optimization — where successful collaboration depends on maintaining viable coupling between human and AI across time, rather than minimizing error or maximizing performance.

My research contributes:

• new models of human–AI interaction dynamics
• tools for measuring creative collaboration processes
• design principles for building more adaptive and resilient co-creative systems

By shifting focus from output quality to interaction viability, this work aims to support the next generation of AI systems that collaborate with humans in ways that are stable, flexible, and meaningfully participatory.