21 Interest Groups

Disease diagnosis/treatment

Early and accurate detection, diagnosis and treatment of illness(es) 

Mental health

Tackling mental health (e.g. depression, dementia, and cognitive) problems

Medical images/omics

Collection, analysis, and interpretation of medical images and omics data

Sensing & auto operation

Data-driven design for sensing and autonomous operations

Complex systems

Physics-guided/ compliant learning for complex systems

Molecules and Materials 

Creating and using novel chemicals and materials

Universe's fundamentals

Detecting rare events for new insights into the Universe’s fundamentals

Climate environment & bio

Tackling climate/environmental change & understanding biology

Urban, Built Environment and Population Studies IG

Studying mobility, active lifestyles, urban environments, and population health

Data-driven arts/humanities/social sciences

Revolutionising data-driven research in the arts, humanities, and social sciences

Misinformation, privacy, security

Identifying and tackling mis- and disinformation & enhancing privacy/security

Innovation & entrepreneurship

Shaping the future of innovation and entrepreneurship and building trust

AI ED: Pedagogy & policy for education

Advancing pedagogy and policy to develop education in DS & AI

Accessible/sustainable/scalable

Best practices for accessible, sustainable, and scalable systems

Explainability / energy-efficiency

Increasing AI transparency, explainability, interpretability, and energy-efficiency

Visualisation

Effective and efficient information visualisation to various audiences

Data integration  

Integration of data/knowledge & disentangling of hidden factors

Uncertainty estimation

Accounting for uncertainties in models for physical and social processes

HCI / robotics

Safe, smart, connected technology (e.g. robotics) and human-machine interaction

Automate data prep/access/use 

Automating/improving data preparation, access, understanding & exploitation

Causality inference

Using and analysing data to confidently infer causal relationships