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.

This workshop cluster offered at the 2018 Smart Geometry Conference in Toronto was the initial catalyst for the Fresh Eyes project, and was the first incorporation of user-generated image recognition models into the evaluation step of a traditional generative design workflow.

This project uniquely links the familiar parametric environment of Grasshopper with cloud-hosted models trained using Lobe.ai: a user-friendly ML graphic programming environment that runs Tensorflow.

In contrast with generic approaches to quantification, this 6-day workshop cluster, occurring at the Smart Geometry Conference and hosted by The Royal Danish Academy of Fine Arts, hypothesizes that data-driven design must be supported by designed data.

This 6-day workshop cluster, occurring at the Smart Geometry Conference and hosted by The Chinese University of Hong Kong, speculates about about the use of a novel analytical tool for structuring the various spatial constituents and datasets related to high-density urban environments.