Our Projects

Foundational Science

These research questions are complementary, interrelated, and applicable across all use cases. Research here is informed by and will be evaluated through the domain specific use cases.

Dynamic Situational Awareness

Developing new methods to provide optimal situational awareness for humans and machines to understand each other’s situation as events evolve within dynamic missions; therefore, to collaborate effectively.

Trust in CINTEL

Understanding which contextual factors may contribute to, influence or erode how trust in CINTEL is established and maintained.

Workflows

Reimagining workflows to take advantage of the complementary capabilities of humans and AI and create effective, efficient and enjoyable collaborative intelligence systems.

Skills for Collaborative Intelligence

Understanding the role and experience of the human in optimising collaborative intelligence.

Collaborative Monitoring

Humans supported by AI to monitor a situation.

Collaborative Environmental Modelling

Monitoring compliance and implementing effective enforcement is an essential ingredient in making coastal marine reserves more than paper parks. However, one of the central challenges for management agencies is establishing an effective monitoring and surveillance system.

Observing the radio sky through CINTEL

Observing the radio sky in partnership with Artificial Intelligence. Developing new tools and collaborative intelligence workflows to optimise radio telescope operations while allowing humans to focus their energy where it is needed most.

CINTEL for Cybersecurity

Current approaches to AI integration in Security Operations Centres (SOCs) rely heavily on automation and augmentation, approaches with limitations hindering their effectiveness. We move automation and augmentation towards human-AI teaming, fostering active collaboration that unlocks the true synergy between human expertise and AI capabilities.

Collaborative Discovery

Humans supported by AI to discover new knowledge.

Collaborative Curation & Collection Science

Leveraging the complementary capabilities of human and machine intelligence to collaboratively facilitate direct connections between digitisation, curation and data integration and management.

Machine Assisted Genome Annotation

Working to develop a collaborative human-AI workflow leveraging the latest developments in ML application to the problem of Genome Annotation. The workflow will allow the domain experts to efficiently curate automated GA in the context of an interactive and learning system.

Collaborative Crystal Conditions

Protein crystallography is used extensively in drug development, as understanding the structure-activity relationship between the protein and the ligand enables a rational approach to drug design. Obtaining a good X-ray diffracting crystal of the macromolecule is a major bottleneck in the process, as it requires an exhaustive sampling and subsequent optimisation of the physicochemical space to find the particular crystallisation conditions for each protein. This project aims to develop a CINTEL approach to predict the conditions in which a certain protein will crystallise.