Where Mathematics Meets Mission
Designing AI and mathematical models that turn complex data into decisions — for public health, climate resilience, and equitable communities worldwide.
Research Background
Training & Context
Our research is grounded in applied mathematics, artificial intelligence, and data science, with a strong focus on translating quantitative methods into real-world solutions for public health and complex systems. Led by Prof. Jude Kong, Canada Research Chair in Community-Oriented AI and Mathematical Modeling of Infectious Diseases and a Fellow of the Royal Society of Canada. AIMMLab bridges mathematical rigor and global health impact.
Prof. Kong holds a PhD in Applied Mathematics (University of Alberta), MSc degrees in Mathematical Engineering (University of L'Aquila) and Technomathematics (University of Hamburg), and completed postdoctoral training at Princeton University. This depth of training anchors the lab's approach: build models that are both analytically sound and deployable in practice.
Research Framework
Five Thematic Areas
All work at AIMMLab flows from a single engine " Foundational AI and Mathematical Modelling" and radiates outward into five interconnected domains:
- Health & Well-Being Modelling infectious diseases and health outcomes to inform public health policy and practice.
- Climate & Environment Understanding species distributions, ecosystem shifts, GHG emissions, and algal blooms.
- AI Safety, Ethics & Governance Responsible AI frameworks focused on fairness, accountability, transparency, and risk.
- Equity & Inclusive Development Using AI and modelling to identify inequities and promote community-centred development.
- Capacity Building Training the next generation through mentorship, education, and knowledge sharing.
Core Research Themes
What We Work On
AI for Health Systems Strengthening
We develop and deploy AI and data-driven methodologies to improve prediction, forecasting, monitoring, and control of infectious disease outbreaks. During COVID-19, Prof. Kong led a team of 52+ researchers across nine African countries building AI tools for governments and public health agencies. This work directly shaped the AI4PEP Network — now 230+ researchers in 23 countries — advancing scalable, locally relevant AI solutions that strengthen health system resilience and support evidence-based policy.
Mathematical Modeling of Infectious Diseases & Complex Systems
We design deterministic and stochastic models, multi-scale frameworks, and hybrid approaches that integrate mechanistic understanding with data-driven insights. These models investigate transmission dynamics, assess intervention strategies, and support risk assessment for emerging and re-emerging pathogens. Our work increasingly spans zoonotic diseases, climate-sensitive health risks, and ecological disruptions — reflecting a One Health perspective linking human, animal, and environmental health.
Responsible AI & Global Health Equity
We develop frameworks that ensure AI technologies are transparent, accountable, and aligned with the social, cultural, and ethical contexts in which they are deployed. Through the Africa-Canada AI and Data Innovation Consortium (ACADIC), our work prioritises Southern-led innovation and capacity building — training researchers, fostering interdisciplinary collaborations, and co-creating solutions that address systemic inequities in global health systems.
Climate, Sustainability & Health Systems Modeling
We examine the intersection of climate change, sustainability, and public health. Through interdisciplinary collaborations, we build modeling frameworks to assess how environmental and climate-related factors shape disease dynamics and health system resilience — designing adaptive strategies that protect vulnerable populations and strengthen preparedness for future global health challenges.