Kaicong Wu

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

◆ ARA Academy

Assistant Professor

University of Arkansas

Also affiliated: Princeton University (2016–2019); The Ark (2026)

3 h-index 9 pubs 42 cited

Research Areas

Links

ORCID

OverviewAI-generated summary

Kaicong Wu's research investigates regenerative design through the integration of generative computing, artificial intelligence, and robotic assembly. His work aims to develop architectural solutions that offer enhanced customizability and reversibility, addressing sustainability challenges. Wu teaches design studio and advanced digital technologies courses at the University of Arkansas, focusing on preparing students for the evolving landscape of design, computation, and material innovation.

His research explores how emerging technologies like generative AI, simulation, and adaptive robotic workflows can support environmentally responsive and publicly engaged design systems. By promoting diversity and reversibility in design outcomes, Wu seeks to align mass customization with sustainability goals, arguing that socially responsible design does not necessitate increased natural resource consumption. His scholarly contributions include publications on topics such as stochastic assembly, deep learning for architectural design, robotic assembly for structural forms, and the use of thermographic sensing in robotic fabrication.

Metrics

h-index: 3
Publications: 9
Citations: 42

Positions

Assistant Professor 2025–present

University of Arkansas Fay Jones School of Architecture and Design Institutional directory
Assistant Professor 2020–2025

The University of Hong Kong Architecture ORCID

Selected Publications

A multimodal foundation model-enabled agent for human–robot collaboration in construction (2026)

Construction Robotics DOI OpenAlex

View all publications on OpenAlex →

ARA Academy 2025 Innovation Scholar

Dr. Wu is a trained architect and computational designer whose research focuses on regenerative design informed by generative computing, artificial intelligence, and robotic assembly. He investigates generative AI, simulation, and adaptive robotic workflows to develop customizable and highly reversible architectural solutions. He holds a Ph.D. in Architecture from Princeton University and a Master of Architecture from the University of Pennsylvania.

Policy Impact

Advancing regenerative design through AI and robotic assembly, positioning Arkansas at the forefront of sustainable construction technology and creating pathways for new manufacturing applications.