ksteinfe.com

Biography

Kyle Steinfeld is an architect who works with code and lives in Oakland.

Through a hybrid practice of creative work, scholarly research, and software development, he seeks to reveal certain overlooked capacities of computational design; he finds no disharmony between the rational and whimsical, the analytical and uncanny, the lucid and bizarre. His work cuts across media, and is expressed through a combination of visual, formal, and spatial material. Across these, we find a consistent theme of undermining the imperative voice that is so often bestowed upon the results of computational processes, and find in its place a range of alternative voices.

His work at the intersection of Artificial Intelligence and Environmental Design has been exhibited at the Pavillon De l’Arsenal in Paris, at the NeurIPS workshop on Machine Learning for Creativity and design in 2017 and 2018, and has been published in Towards Data Science. He regularly organizes workshops on this subject.

Kyle is an Associate Professor of Architecture at UC Berkeley, and the Associate Director of the Master of Design program. In his academic and scholarly work, he seeks to illuminate the dynamic relationship between the creative practice of design and computational design methods, thereby enabling a more inventive, informed, responsive, and responsible practice of architecture. He is the author of a number of software design tools, and has published widely on the subject of design and computation. He is the author of “Geometric Computation: Foundations for Design“, a foundational text that demystifies computational geometry for an audience of architecture students and design professionals. He has been the recipient of a number of research grants and fellowships; he was an IDEA fellow at Autodesk in 2014, and a Hellman Fellow in 2012.

As an educator, Kyle has taught core courses in design and architectural representation, and seminars in design computation for more than ten years at UC Berkeley, and more than twenty years at other institutions.

In a previous life as a professional architect, Kyle worked with and consulted for a number of design firms, including Skidmore Owings and Merrill, Acconci Studio, Kohn Petersen Fox Associates, Howler/Yoon, Diller Scofidio Renfro, and TEN Arquitectos.

Kyle holds a Masters of Architecture from the Massachusetts Institute of Technology (MIT), and a Bachelor’s Degree in Design from the University of Florida (UF).

Projects

Sketch2Pix – 2020

This project explores the application of the augmented architectural drawing tool Sketch2Pix: an interactive application that sup-ports architectural sketching augmented by automated image-to-image translation processes.

It is in this context that an undergraduate research studio was conducted at UC Berkeley in the Spring of 2020. By introducing novice students to a set of experimental tools and processes based on ML techniques, this studio seeks to uncover those original practices or new subjectivities that might thereby arise. We describe here a series of small design projects that examine the applicability of such tools to early-stage architectural design. Specifically, we document the integration of several conditional text-generation models and conditional image-generation models into undergraduate architectural design pedagogy, and evaluate their use as “creative provocateurs” at the start of a design.

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GAN Loci – 2019

This project applies generative adversarial networks (or GANs) to produce synthetic images that capture the predominant visual properties of urban places. Imaging cities in this way represents the first computational attempt to documenting the Genius Loci of a city: those forms, spaces, and qualities of light that exemplify a particular location and that set it apart from similar places.

Displayed at the Intelligence Artificielle & Architecture exhibit at the Pavillon De l’Arsenal in Paris. 

Not Far From Home – 2018

In work exhibited at the NeurIPS 2018 Machine Learning for Creativity and Design, three-dimensional architectural massings for single-family homes are generated by a generative adversarial network. This GAN is trained on a small dataset of three-dimensional models of homes falling into seventeen architectural styles, and that are represented as multi-view heightfield images. A process is developed for converting from 3d CAD model to 2d tiled heightfield image, and from the 2d heightfield images generated by GAN back to three-dimensions in voxel format.

Fresh Eyes – 2018

In work exhibited at the University of Toronto in the context of the 2018 Smart Geometry Conference, three-dimensional architectural massings for single-family homes are generated by a generative adversarial network. This GAN is trained on a small dataset of three-dimensional models of homes falling into seventeen architectural styles, and that are represented as multi-view heightfield images.

Death Valley – 2017

In work exhibited at the NeurIPS 2017 Machine Learning for Creativity and Design, we developed a process for relating depthmaps extracted from Google Street View panoramas with the corresponding photographic information. A number of separate models were produced using limited geographic areas of selected cites. With these depthmap-to-panoramic cityscape models trained, we are able to generate new images from unrelated depthmaps which resembled photographic images of the selected cities. This is demonstrated using a javascript app (not currently online) and documented in still images and a series of videos.