Unit Cambridge

The ELLIS Unit Cambridge is dedicated to advancing machine learning and artificial intelligence, with a particular focus on probabilistic and Bayesian methodologies. This specialisation addresses challenges such as robustness, data efficiency, uncertainty awareness, flexible adaptation, and understanding causality.

• Probabilistic and Bayesian Machine Learning: Developing models that quantify uncertainty, enhance data efficiency, and improve robustness in AI systems.
• Natural Language Processing: Advancing language modeling techniques to improve machine understanding and generation of human language.
• Healthcare: Applying AI to medical data for better diagnostics, treatment planning, and personalised medicine.
• Computer Systems: Enhancing system performance and efficiency through intelligent algorithms and predictive models.
• Molecular Modeling: Utilizing machine learning to predict molecular behavior, aiding in drug discovery and materials science.

The unit's collaborative methodology and diverse expertise across these areas aim to create a broad impact in both foundational research and practical applications.

The mission of the ELLIS Unit Cambridge is to build on the University of Cambridge’s strong AI and machine learning infrastructure, serving as a foundation for a center of excellence. With a unique focus on Bayesian statistics and probabilistic machine learning, the unit addresses key challenges such as robustness, data efficiency, uncertainty awareness, flexible adaptation, and causality. Its diverse expertise in areas like language modeling, healthcare, computer systems, and molecular modeling ensures broad impact and strengthens its role as a valuable asset to the ELLIS society.