Jin Lee

Computational design, AI-powered human factor analysis, sustainable building technologies, behavioral interaction modeling

Jin Lee is a PhD student in architecture at UC Berkeley. Her research explores interdisciplinary computational design methods with advanced AI to address the complexities of human and environmental factors, predicting the responses of living organisms. She is interested in enhancing holistic and sustainable building performance and better incorporating new environmental technologies into social and ecological systems.

Global Korea Scholarship (GKS), Korea National Institute for International Education (NIIED), Korean Government, 2024

MOLIT Minister's Prize, Agents Exploring Aesthetic of Architecture Using Computer Vision, The 18th Design Application Technology Competition, Architectural Institute of Korea, 2021

Lee, J., & Hong, S. W. (2024). Professional architects’ design trade-offs in reinforcement learning-powered agent-based simulation. Architectural Science Review, 1–13.

Lee, J., Hong, S. W., & Cho, C. Y. (2024). Reinforcement-learning agents for architects' trade-offs in designing children's play environment: A qualitative comparative analysis. Design Studies, 91, 101248.

Hong, S. W., Lee, J., & Lee, J.K. (2024). Human behaviour simulation for promoting usefulness and user-centric values in parametric design. Automation in Construction, 162, 105368.

Lee, J., & Hong, S.W. (2023). Developing the Reinforcement-Learning Children Agents for Measuring Play and Learning Performance in Kindergarten Design. eCAADe 2023 Digital Design Reconsidered (pp. 69-78).