The Generative Intelligence Lab focuses on translational research on frontier AI systems. We examine how generative intelligence reshapes workflow architectures, human-machine collaboration, and organizational reasoning. Our projects follow a shared path from real-world demand and research argument to innovation, experimentation, and provable results. Our research agenda focuses on:

AI-driven organizations — how organizations can be designed around AI-first workflows that integrate human and artificial reasoning into the operating model. We investigate the architectures and evaluation methods for adaptive workflows, agentic pipelines, and multi-level reasoning systems in which one layer observes and adjusts another.
Agentic-Oriented Solution Engineering — how adaptive AI workflows can be designed to act autonomously, observe changing conditions, adapt their behavior, and reconfigure their operations. We investigate the reusable elements, workflow representations, system architectures, and multi-agent compositions that support their creation and evaluation.
Social skills of AI agents — how people and AI agents can work together in AI-first workflows with clear roles, coordinated contributions, and accountability. We investigate the role models, coordination mechanisms, human oversight, and decision structures that enable collective intelligence.

FEATURED PROJECTS

Five-stage process from de-identified child-welfare records to practitioner review.

Enhancing Child Welfare Research and Translation through AI

The project studies a human-centered approach to bringing frontier AI into child-welfare research and translation. We investigate how de-identified records can yield clear, explainable insights through information extraction, summaries, risk prioritization, and dashboards. This work advances innovations in explainable, accountable human-agent workflows, where practitioners retain decision authority.

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