Work related to the Almost Home project and the Fresh Eyes toolkit was featured in the MLxART Collection, an online showcase of "creative machine learning experiments" curated by Emil Wallner at the Google Arts & Culture Lab.

This paper presents a framework for the application of Machine Learning (ML) to Generative Architectural Design (GAD), and illustrates this framework through a description of a series of projects completed at the Smart Geometry conference in May of 2018 (SG 2018) in Toronto.

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.

This workshop offered at the Design Modeling Symposium in Berlin presents tools and techniques for the application of Machine Learning (ML) to Generative Architectural Design (GAD).

A toolkit for connecting parametric models in Grasshopper with hosted image classification machine learning models.