While Matt Damon's character engaged us in the challenges of growing potatoes on Mars, the reality of off-world living is even more complicated than author Andy Weir described.
Our speaker Kai Staats is project lead for the Arizona State University School of Earth & Space Exploration Interplanetary Initiative pilot project SIMOC (see-mok). His team has developed this scalable, interactive model of an off-world community built on decades of NASA research and data. The web interface
asks users to select the number of astronauts and size of living quarters, greenhouse and types of plants, power generation, and then set the model in motion to learn how well the design holds up.
Science fiction has made it look easy with food growing in massive greenhouses or materializing on command. In the real universe, finding a balance of machines and plants to sustain human life is a complex endeavor. The slightest incongruity in waste management, power production, or CO2 scrubbing can result in catastrophic failure and abandonment of the habitat, or worse.
In this interactive presentation, Kai will introduce the audience to closed ecosystems experiments, provide a tour of SIMOC, and engage the audience in consideration of how closed ecosystem studies have and continue to help us understand our first home as we prepare to make new homes among the stars.
Kai Staats is a visiting scientist at LIGO where he works in machine learning applied to noise mitigation and supernova detection. At Arizona State University he leads a team in developing SIMOC (www.simoc.space), an agent-based model of a closed ecosystem built upon NASA data. The web interface enables users to design and model a Mars habit.
Kai is an award-winning filmmaker, researcher and writer. He has produced three documentaries for LIGO, the gravitational wave observatories. His footage and films have aired on the National Science Foundation's educational channels, Discovery Channel, Space.com, New Scientist, and PBS member stations. He holds a BSc in Industrial Design from Arizona State University and an MSc in Applied Mathematics from the University of Cape Town, South Africa where he applied machine learning to the mitigation of human-generated noise in radio astronomy. In 2014 Kai was a member of an Mars Desert Research Station Crew, and January he will install a telescope in Tanzania's first astronomical observatory.