This is a studio-style course that emphasizes collaborative learning and innovation in sustainability. Students will explore foundational and cutting-edge literature, research, and potential future directions in AI for Sustainability. The course will cover a range of topics related to the use of AI and machine learning in sustainability science and engineering, including energy systems decarbonization, sustainable agriculture, climate modeling, resource optimization, and biodiversity conservation. Students will gain hands-on experience with AI/ML methodologies, tools, and software and engage in discussions on the latest advancements and applications of AI in addressing global sustainability challenges.

This studio-style course will be offered in Spring 2025 and Fall 2025. Stipend-supported trainees are expected to attend both sessions.

This course focuses on the application of artificial intelligence (AI) in materials science. Students will explore how AI and machine learning techniques can accelerate the design, discovery, and optimization of materials for energy storage, conversion, and sustainability. Key topics include using AI to predict material properties, enhance materials synthesis, and model complex material behaviors. The course emphasizes practical applications of deep learning models in materials science, featuring hands-on projects and case studies from recent research. Students will also discuss the challenges and opportunities of applying AI to advance innovations in materials science.

The modular course will be offered in Spring 2025.

This course focuses on the application of artificial intelligence (AI) to energy systems. Students will explore how AI techniques can optimize the performance of energy and power systems, with a particular focus on sustainable energy systems and renewable energy transition. Key topics include the optimization of energy generation, distribution, storage, and consumption. Specific case studies will cover topics such as optimizing solar and wind energy integration into the grid, improving battery storage management for renewable energy, and enhancing energy efficiency in smart grids. The course will also highlight AI applications in balancing supply and demand for renewable energy systems.

This course focuses on the application of artificial intelligence (AI) in the digital transformation of agriculture. Students will explore how AI techniques are applied to optimize and automate agricultural systems, improve productivity, and enhance sustainability. The course covers a broad range of topics, including AI-driven crop management, precision farming, livestock monitoring, and data analytics for sustainable agriculture. Case studies on AI applications in plant and animal production systems, as well as food supply chains, will provide practical insights into the future of farming. Students will engage in discussions on the ethical, social, and economic implications of AI in agriculture, while hands-on projects will offer experience in applying AI tools to real-world agricultural challenges.