With these research projects we hope to provide the computational and communication foundations for sustainable development in core environmental, economic, and social sustainability areas
Energy-constrained and sustainable deep learning
The estimation and optimization of energy consumption and carbon footprint derived from the use of AI systems. In this line, we will foster change in the entire lifecycle of AI products (i.e., idea generation, training, re-tuning, inference, etc.) by characterizing the use of IT towards greater ecological integrity. The final goal is to develop AI technologies (particularly deep learning) that are compatible with sustaining environmental resources by exploring the trade-offs of accuracy of AI outcomes vs. reducing carbon emissions and computing power.
Energy-constrained trustworthy systems
The creation of cryptocurrency systems that not only does not consume a lot of energy but can help save energy. The target is to create a new generation of cryptocurrency systems that can reward users who plant trees, and the underlying infrastructure does not use expensive consensus protocols.
Sustainable smart cities and transport systems
The application of IT and cloud computing technologies to support the creation of sustainable digital twins, including IoT data collection, to model cities and transportation systems. The target is to bring IT as an enabler of a progressive systemic change towards developing an entire value chain to address circularity and systematic carbon footprint reduction.
The creation of sustainable IT approaches and strategies to support the required massive computational and storage resources of different strategic scientific domains for our society with cloud computing. In particular, we will consider climate simulation, particles collider, and the Square Kilometer Array (SKA) challenges in the ETH domain.