Jianhong Wu

Canada Research Chair in Industrial and Applied Mathematics

Tier 1 - 2017-11-01
Renewed: 2016-02-01
York University
Natural Sciences and Engineering Research Council

416-736-5250, ext./poste 33116

Research involves

Developing mathematical models, theories, methodologies and tools to describe spatio-temporal patterns in nonlinear dynamical systems with variable time lags and spatial dispersal.

Research relevance

This research will lead to better decisions that will help reduce the spread of communicable disease.

Using Complex Data Modelling and Analytics to Make Better Decisions

What factors might lead to an infectious disease outbreak, or even to the extinction of the human race? How can neural networks— systems that learn to "think" and solve difficult problems based on previous experiences, much like the human brain—be used in data mining to find patterns by collecting and filtering through massive amounts of correlated information?

These are just some of the questions Dr. Jianhong Wu, Canada Research Chair in Industrial and Applied Mathematics, seeks to answer. Wu is conducting comprehensive and multidisciplinary research in industrial and applied mathematics.

As an internationally recognized mathematical authority in delay differential equations, infinite dimensional dynamical systems, non-linear analysis, mathematical biology, neural dynamics and information management, Wu has already played a key role in helping industry and governments with real-world applications.

Now, Wu and his team are working with other researchers, industrial data mining experts and government decision-makers to analyze the information processing capabilities of neural networks modelled by multiple-scale differential equations. By collaborating, they hope to develop effective mathematical formulas and technologies for pattern recognition, classification and prediction. Wu and his team are also partnering with industry to assess how their discoveries can solve health, medical and social media data mining and analysis tasks.

Ultimately, Wu’s research will advance theoretical foundations in mathematical modelling and analysis for complex data and information processing. It could also improve the global competitiveness of Canada’s data mining industry and play a key role in informing future public health decisions.