During the second year of the Master in Advanced Architecture + Thesis Project (MAA02), students have the unique opportunity to work for a period of 1 year on an Individual Thesis Project, focused on the development of a research or pilot project based on the student’s interest, and the learnings of the first year. IAAC supports the student in selecting their Thesis Project topic in order to better orient them according to their future career interests and opportunities. Each student, according to their specific topic, is assigned one or more Thesis Advisors that follow the development of the work throughout the year.
In parallel to the development of the Individual Thesis Project, the second year of the MAA02 offers a series of seminars enhancing the theoretical, practical and computational skills of the students.
In an increasingly digitised world, the integration of parametric and data-driven design at various scales within the built environment is no longer optional but essential. This thesis cluster brings together research themes that explore the transformative role of computational design and data-informed methodologies in shaping the AEC industry.
While digital tools and processes are advancing rapidly, it is vital to examine their frameworks, workflows, and implications in architecture—the design and materialisation of human-made spaces. Students will propose and investigate how parametric and data-driven approaches can offer innovative solutions to contemporary challenges in the built environment.
Research may involve collecting, analysing, or generating extensive datasets to inform predictive models and optimise design outcomes. By leveraging parametric design and computational techniques, students can explore new ways to enhance architectural creativity, improve spatial performance, and address complex design requirements.
The thesis projects in this cluster aim to develop cutting-edge parametric processes and data-driven strategies for design exploration and optimization, leading to greater efficiency, adaptability, and responsiveness in the built environment.
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APPENDIX: BIBLIOGRAPHY Academic Papers Alnuzha, M. & Bloch, T. (2025). The Role of Machine Learning in Automated Code Checking: A Systematic Review. ITcon, Vol. 30. [Link] Peng, J. & Liu, X. (2023). Automated code compliance checking research based on BIM and knowledge graph. Scientific Reports, 13, 7065. [Link] Shi, J.W.L. et al. (2025). BuildThemis: Fine-tuning … Read more
