Research
CAISI leverages artificial intelligence (AI) to tackle some of the most pressing global sustainability challenges. By integrating AI technologies with interdisciplinary research, CAISI aims to drive innovative solutions that address critical issues across energy, food, and climate systems. Through cutting-edge research and collaboration with academic, industry, and nonprofit partners, CAISI is working to develop scalable solutions that contribute to the decarbonization of energy systems, advancements in sustainable agriculture, and the reduction of environmental impacts. CAISI’s research projects span multiple scales, from the computational discoveries of sustainable materials to global climate modeling, all with the goal of creating a more sustainable and resilient future.
Focus Areas
The following research areas are of interest in the AI4S program:
AI for Energy Decarbonization
CAISI’s research in energy systems focuses on using AI to accelerate the transition to low-carbon solutions across critical sectors. This includes the decarbonization of buildings, transportation, manufacturing, and digital infrastructure, such as data centers and other AI-driven systems with high energy demands. By leveraging AI, CAISI optimizes energy efficiency, integrates renewable energy, and reduces emissions in these energy-intensive sectors. Additionally, CAISI develops AI-driven tools for smart grids and energy storage to support global efforts in minimizing carbon footprints and advancing sustainable energy systems.
AI for Agri-Food Systems
CAISI applies AI to improve the sustainability of agri-food systems through digital agriculture, climate-smart farming techniques, and innovations in food production. From open-field crop production to controlled environments like greenhouses and plant factories, CAISI’s research optimizes resource use, enhances crop yields, and minimizes environmental impacts. In the livestock and dairy sectors, AI-driven solutions help decarbonize production, improve feed efficiency, and reduce methane emissions. Additionally, AI models are used to predict and mitigate the effects of climate change on agricultural systems, strengthening the resilience and sustainability of food production worldwide.
AI for Sustainable Materials
CAISI’s research in sustainable materials focuses on the development of advanced energy materials that are both high-performance and environmentally friendly. AI techniques are used to accelerate the discovery and design of materials with lower environmental footprints, such as those used in renewable energy technologies, energy storage, and building materials. The goal is to support sustainable manufacturing and reduce the reliance on non-renewable resources.
AI for Energy-Food-Climate Nexus
At the intersection of energy, food, and climate, CAISI addresses the interconnectedness of these systems through comprehensive AI models and simulations. Research in this area explores how changes in one system can impact the others, with the goal of creating solutions that optimize the balance between energy use, food production, and climate mitigation. By leveraging AI, CAISI aims to provide insights that lead to more sustainable global systems.
Example Research Projects
CAISI researchers are involved in the following research projects:
AI for Advanced Energy Storage Materials
Leveraging machine learning algorithms to accelerate the discovery of high-performance materials for batteries and fuel cells, focusing on optimizing material properties like conductivity and stability to improve energy storage efficiency.
AI-Driven Climate Models for Agriculture
Applying deep learning and climate modeling techniques to predict how climate change will impact crop yields, water usage, and pest patterns, providing farmers with adaptive strategies to mitigate these effects.
AI in Dairy and Livestock Decarbonization
Utilizing predictive modeling and sensor data to monitor livestock health, optimize feed, and reduce methane emissions in dairy and livestock systems, addressing the challenge of reducing greenhouse gases in the agriculture sector.
AI for Sustainable Hydropower and Aquaculture
Using optimization algorithms to model the trade-offs between hydropower production and aquaculture in ecosystems like the Amazon Basin, minimizing environmental disruption while maximizing renewable energy generation.
Industry and NGO Partners
