Research Interests

• Biological Stoichiometry: Stoichiometric modelling, Harmful algal bloom, Biodiversity, Climate change, Bifurcation analysis, Nonsmooth dynamical systems, Multiscale analysis, Transient dynamics, Stochastic process, Tipping

• Ecotoxicology: Risk assessment of industrial and natural toxicants, Sustainability of ecosystems, Multiscale modelling, Mechanistic modelling

• Biodegradation: Measure and prediction of greenhouse gas emission, Risk assessment of oil sands pollution, Organic matter decomposition, Priming effect, Model validation

• Species Invasion: Control of invasive species, Complex dynamics, Hybrid model

• Cognitive Animal Movement: Modelling perception, learning and memory in animal movement, Partial differential equations with nonlocal integrals, Network modelling, Habitat destruction and preservation, Model derivation via first principles

• Transmission of Infectious Diseases: Inverse method, Data science, Machine learning, Indirect transmission

Research Projects

NSERC Alliance Missions: Anthropogenic Greenhouse Gas Research

Canada's priorities include environmental protection and enhancing ongoing efforts to mitigate adverse climate change effects by reducing greenhouse gas (GHG) emissions to at least 40% of 2005 by 2030 and net-zero emissions by 2050. From Canada's diverse anthropogenic GHG sources, increased methane emissions from oil sands tailing ponds (OSTP) and end-pit lakes (EPL) have become of great concern due to their warming potential, about seven times as much as carbon dioxide in 20 years. Creating suitable policies that contribute to the economic benefits of sustainable bitumen extraction from Canadian oil sands requires building promising land reclamation scenarios and methane emissions mitigation strategies based on substantial scientific facts. The Interdisciplinary Laboratory for Mathematical Ecology and Epidemiology (ILMEE) has granted about 1.4 million CAD from NSERC Alliance Missions funds to contribute directly to science-advancing climate research, specifically methane mitigation strategies and monitoring in oil sands territories. Our approach will use various scientific methodologies, including microbiology and aquatic toxicology laboratory experiments, GHG monitoring, data collection and processing, AI machinery and mathematical modelling, and we aim to generate a novel holistic model encompassing diverse challenges and providing insightful future directions to stakeholders to achieve desired goals. 

Alberta Conservation Association

The ACA project focuses on addressing the persistent challenge of cyanobacterial blooms, specifically, in Alberta, Canada. Cyanobacterial blooms pose a significant environmental concern in Alberta's lakes and water bodies with current monitoring practices predominantly in specific sites. However, smaller water bodies and lakes with limited access often lack proactive monitoring initiatives leaving them vulnerable to the increasing frequency and intensity of cyanobacterial blooms exacerbated by changing environmental conditions. The project aims to develop a novel neural network leveraging a convolutional architecture coupled with a generative network tailored to Alberta's context. This innovative approach aims to enhance detection and monitoring by extracting both geometry and statistical relations from multi-spectral satellite images, integrated with in-situ measurements, by predicting cyanobacterial concentration patterns and estimating nutrient levels in Alberta's lakes, particularly, those with limited monitoring resources. This initiative is crucial for the proactive protection of water resources and public health in Alberta.