Karla Saldaña Ochoa is a tenured track assistant professor in the School of Architecture in the College of Design, Construction and Planning and a faculty affiliate at the Center of Latin American Studies and FIBER, the Florida Institute for Built Environment Resilience at UF. Karla's teaching and research investigate the interplay of artificial and human intelligence to empower creativity and social good. Karla leads the SHARE Lab; a research group focused on developing human-centered AI projects on design practices. Karla is an Ecuadorian architect, and her Ph.D. investigated how the integration of artificial and human intelligence have a precise and agile response to natural disasters.
Areas of Expertise (5)
Media Appearances (3)
Can AI mark the next Architectural Revolution?
The evolution of computation has led to a decentralized shift from singular functional machines to a world of applications. We have moved from one central computer serving many people (1943) to one computer per person (1974) and now to one person using multiple devices (2023). These devices constantly gather information about our daily activities.
Karla Saldaña Ochoa
Voices in Design Computing online
Karla Saldaña Ochoa is an Ecuadorian architect with a Master of Advanced Studies in Landscape Architecture from ETH Zurich. In June 2021, she finished her Ph.D. at ETH Zurich, where she investigated the integration of artificial intelligence (AI) and human intelligence to have a precise and agile response to natural disasters.
Going beyond typologies and optimization: A conversation with Karla Saldaña
Data Aided Design online
Drawings have been the traditional medium architects use to organize ideas and explore design options. When designing, we intuitively think about associations and interrelations between different design constraints and possible design solutions.
Creating a coefficient of change in the built environment after a natural disasterArXiv
Karla Saldaña Ochoa
This study proposes a novel method to assess damages in the built environment using a deep learning workflow to quantify it. Thanks to an automated crawler, aerial images from before and after a natural disaster of 50 epicenters worldwide were obtained from Google Earth, generating a 10,000 aerial image database with a spatial resolution of two m per pixel. The study utilizes the algorithm Seg-Net to perform semantic segmentation of the built environment from the satellite images in both instances.
Beyond typologies, beyond optimization: Exploring novel structural forms at the interface of human and machine intelligenceInternational Journal of Architectural Computing
Karla Saldaña Ochoa, et. al
This article presents a computer-aided design framework for the generation of non-standard structural forms in static equilibrium that takes advantage of the interaction between human and machine intelligence. The design framework relies on the implementation of a series of operations (generation, clustering, evaluation, selection and regeneration) that create multiple design options and navigate in the design space according to objective and subjective criteria defined by the human designer.
A framework for the management of agricultural resources with automated aerial imagery detectionComputers and Electronics in Agriculture
Karla Saldaña Ochoa and Zifeng Guo
The acquisition of data through remote sensing represents a significant advantage in agriculture, as it allows researchers to perform faster and cheaper inspections over large areas. Currently, extensive research has been done on technical solutions that can benefit simultaneously from both: vast amounts of raw data (big data) extracted from satellite images and unmanned aerial vehicle (UAV) and novel algorithms in machine learning for image processing.