Matias del Campo
Matias del Campo is an architect, designer, and theorist, currently serving as Director of the MS ACT program (Architecture, Computational Technology) and as Associate Professor at the New York Institute of Technology (NYIT). He co-founded SPAN in Vienna in 2003 with Sandra Manninger, establishing a practice renowned for its integration of contemporary technologies in architectural production. SPAN's award-winning designs are shaped by the intersection of computational methodologies and philosophical interrogations, a conceptual framework they describe as "design ecology." Matias del Campo's innovative contributions have been recognized through prestigious awards, including the Accelerate@CERN fellowship, the AIA Studio Prize and the ACADIA Innovative Research Award of Excellence. His work is included in the permanent collections of notable institutions such as the FRAC, the MAK in Vienna, the Benetton Collection, the Pinakothek Munich and the Albertina. He has authored several books on AI and Architecture such as “Neural Architecture” (ORO), “Diffusions” (Wiley), “Machine Hallucinations” (Wiley) and “Artificial Intelligence and Architecture” (Wiley).
Supervisors: Hans Hollein, Wolf D. Prix, Greg Lynn, Zaha Hadid, and Helmut Richter
Phone: 12672980608
Address: Edward Guiliano Global Center, 11th floor
1855 Broadway
Supervisors: Hans Hollein, Wolf D. Prix, Greg Lynn, Zaha Hadid, and Helmut Richter
Phone: 12672980608
Address: Edward Guiliano Global Center, 11th floor
1855 Broadway
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Videos by Matias del Campo
Can AI’s learn how to design?
This video contains excerpts of work by the artists: Mario Klingemann, Oscar Martinez and Obvious.
Images:
SPAN (Matias del Campo & Sandra Manninger), Alexandra Carlson, James Le, MIT Technology Review, Ersnt & Sohn Verlag, British Museum, Molly Wright Steenson, Facebook Intelligence Research Unit, Google Deepmind Alphago, Tesla, Xiachon Luo & Heng Li (Smart COnstruction Lab, Hong Kong Polytechnic University), Microsoft, PwC, RIB ITWO, John Deere, Hannah Daugherty, Mariana Moreira de Carvalho, Imman Suleiman, Karel Mestdagh, Leon A Gatys, Alexander S. Ecker, Matthias Betghe, Horoharu Kato, Yoshitaka Ushik
The motivation to explore Attention Generative Adversarial Networks (AttnGAN) as a design technique in architecture can be found in the desire to interrogate an alternative design methodology that does not rely on images as starting point for architecture design, but language. Traditionally architecture design relies on visual language to initiate as design process, wither this be a napkin sketch or a quick doodle in a 3D modeling environment. AttnGAN explores the information space present in programmatic needs, expressed in written form, and transforms them into a visual output. This visual output can be further processed into three dimensional models that transport lingual information into fully developed architectural entities. The key results of this research are shown in this paper with a proof-of-concept project: the competition entry for the 24 Highschool in Shenzhen, China. This award-winning project demonstrated the ability of AttnGAN
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Papers by Matias del Campo
By integrating humanities with computational tools, AI aids in crafting inclusive urban environments while enhancing real-time adaptability. Historic paradigms, from Renaissance precision to Modernist austerity, provide a foundation for understanding contemporary challenges. Modern AI techniques, such as data mining and generative design, allow designers to reimagine urban spaces while balancing ethical considerations like fairness and equity. This synergy of historical depth and computational advancement heralds a new era in urban design, characterized by both innovation and responsibility.
on urban design, transcending traditional paradigms and ushering in a new era
of data‑driven, generative approaches. Departing from linear processes, the text
embraces a comprehensive perspective, acknowledging the multidimensional factors shaping urban landscapes. The integration of AI in urban design takes cues
from how neural networks operate, dynamically responding to real‑time data
inputs and historical iterations. Historical reference points, from Renaissance
ideal cities to Modernism, serve as repositories guiding the interrogation of urban morphology.
The reasoning behind the text navigates the complexities of urban planning,
emphasizing the role of humanities in crafting inclusive, meaningful designs. The
interrogation delves into the historical intricacies, from Alberti’s Ideal City to
Simmel’s analysis of metropolitan existence, while scrutinizing modernist movements like Dada, cubism, and futurism and contrasting them with antiurban
ideologies in the works of Howard, Taut, and Wright.
This chapter then transitions to the contemporary landscape, portraying AI’s
disruptive moment in art and design. Drawing parallels with the modernist explosion, it discusses the dichotomy between organic, hands‑on creation and
AI‑driven, data‑informed methodologies. The tension between technological
precision and human creativity is explored, cautioning against the risk of detach
ing art from visceral experiences.
The integration of AI in urban design is examined, emphasizing its potential
in prediction, optimization, and generative design. AI’s capacity to process vast
amounts of data is highlighted, offering evidence‑based insights and breaking
free from traditional design molds. This chapter concludes by underscoring the
ethical considerations of AI in urban design, emphasizing the need for human intuition to complement computational insights and safeguard principles of equity
and social justice.