AIMMLab Research Framework

AIMMLab Research Framework

Our research program is organized around Foundational AI and Mathematical Modelling, which serves as the core engine for all work at AIMMLab. We design, analyze, and implement novel mathematical, artificial intelligence, and statistical models that provide decision-makers in industry, government, and communities with actionable insights into local and global socio-ecological challenges.

This foundational work supports five interconnected thematic areas:

  • Health & Well-Being – modelling infectious diseases and other health outcomes to inform public health policy and practice.
  • Climate & Environment – understanding species distributions, ecosystem shifts, GHG emissions, and harmful algal blooms.
  • AI Safety, Ethics & Governance – developing responsible AI frameworks focusing on fairness, accountability, transparency, and risk.
  • Equity, Poverty & Inclusive Development – using AI and modelling to identify inequities and promote community-centred, inclusive development.
  • Capacity Building – developing the next generation of researchers and practitioners through training, mentorship, and knowledge sharing.

Our short-term objectives include updating and designing new models across these pillars to predict, manage, and forecast:

  • The spread and control of infectious diseases (Health & Well-Being)
  • Species distribution and ecosystem changes (Climate & Environment)
  • Risks and governance needs for emerging AI systems (AI Safety, Ethics & Governance)
  • Patterns of inequity and the impact of interventions (Equity & Inclusive Development)
  • Effective knowledge-translation and training systems (Capacity Building)
AIMMLab Research Framework diagram showing Foundational AI and Mathematical Modelling with thematic areas.
Figure 1. AIMMLab Research Framework. Foundational AI and Mathematical Modelling supports five thematic areas: Health & Well-Being, Climate & Environment, AI Safety, Ethics & Governance, Equity & Inclusive Development, and Capacity Building.

Expertise

Led by Prof. Jude Kong, AIMMLab brings expertise in mathematical biology, infectious disease modelling, machine learning, statistical modelling, data science, AI, citizen science, and participatory research working closely with government, industry, and community partners.

Our postdoctoral fellows, graduate students, and undergraduate trainees come from fields including mathematics, data science, biology, public health, computer science, ecology, epidemiology, and engineering. They contribute to research across all five thematic areas of the AIMMLab Framework.

We train our HQPs to become experts in AI, modelling, computational methods, data management, and policy-relevant research. Many go on to leadership roles in industry, government, and academia.