The twenty-first century has brought with it great opportunities for the mathematical sciences. Many areas of science, engineering, medicine, business, national defence and social science now rely on mathematical ideas and techniques. These include the modelling and analysis of complex systems, computer simulations, and the analysis and management of massive amounts of data.
Due to its fundamental and pervasive nature, mathematics plays a unique, central role in the education and research mission of any university.
The Division of Mathematical Sciences fosters a vibrant research
culture, attracting a steady stream of visitors and international
collaborators. Our faculty have been highly successful in attracting competitive research funding, publishing in prestigious
journals, and speaking at premier research conferences.
Our Division has over 35 full time faculty members, 15+ visiting/adjunct/associate faculty members, 30+ postdoctoral and postgraduate researchers, and 30+ graduate students. We are extremely active in many fields of pure and applied mathematics research, including:
- Pure Mathematics – number theory, geometry, algebra, analysis, topology, random matrix theory, probability.
- Coding and Cryptography – algebraic coding theory, network coding, cipher design, secure multiparty computation, cryptanalysis.
- Computational Mathematics – algorithm design, communication complexity, quantum computing, models of computation, algorithmic information theory, computability theory.
- Applied Mathematics and Statistics – optimization in operations research, multiscale modeling methods, multivariate analysis, stochastic analysis, financial mathematics, applications to biology.
Developing Mathematical Algorithms to Aid E-Commerce
Assistant Professor Yan Zhenzhen is developing decision models to help online retailers improve their logistics.
1 Sep 2022
Looking For Relationships (in a Big Data Stream)
A new algorithm for calculating correlations in big data sets provides an exponential improvement over earlier methods.
15 Oct 2021
Balancing Economic and Public Health in a Pandemic
Stochastic epidemic modeling and deep learning algorithms can help craft optimal social distancing policies for epidemics.
18 Jan 2021
Reconstructing Messages from Incomplete Information
A new mathematical method for decoding incomplete message has implications for computer storage technology.
16 Nov 2020