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Applied and Industrial Mathematics

Industrial Mathematics

Industrial Mathematics, interpreted broadly, is mathematics driven by real applications, ranging from the more traditional manufacturing sector to the modern financial industry. The faculty members at York are firmly embedded in the international community of industrial mathematics and deeply involved in many ongoing activities and research projects. It is strongly linked to mathematical finance, mathematical biology, optimization and scientific computing, but not restricted to these research areas. Many members had and have MITACS industry research projects in financial mathematics, disease modeling, medical imaging, and crystal growth. There is also a strong connection with the ongoing Industrial Problem Solving Workshops held at the Fields Institute regularly and the Springer open-access ejournal: Mathematics-in-Industrial Case Studies.

FACULTY: Michael Chen, Huaxiong Huang*, Jude Kong, Dong Liang, Iain Moyles, Tom Salisbury, Jianhong Wu and Hongmei Zhu (* on leave)

Mathematical Finance

Using tools drawn from the theories of partial differential equations, stochastic calculus, and probability, Mathematical Finance is the study of derivative pricing, risk management, and optimal decision making in finance. The field took off in the 1970s with the famous Black-Scholes-Merton formula, and now underpins much of international finance. York’s financial mathematicians work on a variety of topics, covering both theory and applications. They have strong connections to colleagues in the Schulich School of Business, to research partners in the financial sector, and to York’s actuarial program. They also support York’s Diploma in Financial Engineering, through which many York students have gone on to careers in quantitative finance.

FACULTY: Huaxiong Huang*, Hyejin Ku, Alexey Kuznetsov, Tom Salisbury

Scientific Computing

Scientific computing is a rapidly growing multidisciplinary field which uses advanced computing techniques to understand and solve large complex problems that arise in other disciplines such as engineering, biology, physics, chemistry, finance and many others. Research in scientific computing typically involves work on a specific problem from one of these fields of study where numerical algorithms are developed for use on high performance computers. Students in scientific computing typically learn a broad set of skills from mathematics, computer science and engineering.

FACULTY: Michael Haslam, Huaxiong Huang*, Dong Liang, Jude Kong, Iain Moyles

Mathematical Biology and Disease Modeling

York’s disease modelers form the centre node of the national Centre for Disease Modelling, with collaborations and ties to public health agencies, government, immunologists, virologists, epidemiologists, pharmaceutical developers and global health initiatives. The collective goal is to understand the effects of infectious disease within host, between hosts and in populations, including the effects of drug therapies, vaccines, social behaviour, climate, insect control, and many others. Research is conducted using a diverse set of tools, including dynamical systems, stability analysis, statistical inference, probability theory, computer simulations, and agent-based models.

FACULTY: Jude Kong, Neal Madras, Seyed M. Moghadas, Iain Moyles, Jianhong Wu and Hongmei Zhu

Vaccine Mathematics, Modelling and Manufacturing

In addition, a cross-department research group has been funded by the NSERC/Sanofi Industrial Research Chair (IRC) program "Vaccine Mathematics, Modelling and Manufacturing" to develop a comprehensive research industrial-academic-public collaboration to create and apply mathematical technologies and public health insights/data for proactive impact and safety assessment, rapid response, and retrospective evaluation of public health products and programs.

FACULTY: Jianhong Wu (IRC), Michael Chen, Xin Gao, Jane Heffernan, Huaxiong Huang*, Jude Kong, and Neal Madras

Jane Heffernan

Professor, Applied Mathematics

Mathematics Professor Jane Heffernan’s research responds to the pressing need for new statistical, mathematical, and computational methods of mapping, understanding and controlling infectious diseases and aims to influence the development of new evidence-based public health policies.