For architecture to address the urgent cultural, social, economic, and environmental challenges of our time—to generate true transformational change—it must expand its perception of time.

The Deep Time Project situates architectural agency within a world of temporal entanglements among geological, technological, human, animal, and viral bodies co-producing the environment.

Cristina Parreño Alonso is a Spanish-American architect, artist, and writer whose work explores the temporal and material dimensions of architecture in relation to the Earth’s geological processes. Based between Madrid and Boston, she is a faculty member in MIT Architecture, where she leads The Deep Time Project. Her architectural practice bridges art, philosophy, and material experimentation, reorienting architecture from extraction to collaboration; aligning human making with the generative forces of the Earth.

Her writings on time and architecture advance the concept of Deep Time Architecture, a framework that repositions buildings as material events unfolding across planetary timescales. Her essays have appeared in Log, The Journal of Architectural Education, Strelka Magazine, and Routledge. Parreño Alonso’s work has been exhibited internationally, including at the Venice Architecture Biennale, the Berggruen Institute Arts & Culture program, and the MIT Museum. She studied architecture at ETSAM (Madrid School of Architecture) and has taught at the Harvard Graduate School of Design and the University at Buffalo before joining MIT.

Material Event

Material Event: a process that reveals the interactions between time, matter, and both human and environmental forces. The traces of these material events take the form of texts, videos, buildings, landscapes, material experiments, and exhibitions—each serving as a medium for world-making and world-knowing.

Through these Material Events, we aim to rethink architecture’s role within a broader constellation of forces, understanding it as a dynamic process shaped by geological and human agencies across multiple timescales.