Angie Boggust
Abstract: Generative text-to-image (GTI) models produce high-quality images from short textual descriptions and are widely used in academic and creative domains. However, GTI models frequently amplify biases from their training data, often producing prejudiced or stereotypical images. Yet, current bias mitigation strategies are limited and primarily focus on enforcing gender parity across occupations. To enhance GTI bias mitigation, we introduce DiffusionWorldViewer, a tool to analyze and manipulate GTI mod
Developing an environmental, financial modeling and decision-making tool and workflow in Python to assist cities in achieving their 2050 carbon neutrality goals, by reducing carbon emissions of the building stock through energy efficiency improvements. Analyzing the equity of decarbonization policy implications on future city scenarios. In collaboration with Prof.Christoph Reinhart and Zach Berzolla in the MIT Sustainable Design Lab.
As offices and buildings reopen during the covid pandemic, it is more important than ever that designers and policy makers have the right methods to analyze the safety of indoor spaces. In light of the problem, I developed a software tool, Indoor Eddy3D, to simulate the impact of architectural floor plan layouts and ventilation on indoor air quality and Sars-CoV-2 spread. The tool allows for better decision making, informed by particle data, air movement and 3 dimensional space organization. The tool and wo
Angie Boggust