Mathematics & statisticsMathematical & Stochastic Analysis
Applied & Discrete Analysis
The research of the Applied and Discrete Analysis Group focuses on both qualitative and quantitative methods for analysing discrete and continuous problems involving differential, difference, or integro-differential equations, graphs, permutations, patterns in combinatorial structures, and optimisation. Members of the group employ techniques from combinatorics, graph theory, time series, functional analysis, spectral theory, calculus of variations, bifurcation theory, and more to analyse problems arising in mathematical biology, numerical analysis, liquid crystals, inverse problems, theoretical computer science, and network theory.
Group members:
- Gabriel R. Barrenechea
- David Bevan
- Michael Grinfeld
- Sergey Kitaev (Co-lead)
- Matthias Langer (Co-lead)
- Apala Majumdar
Stochastic Analysis
Stochastic Analysis group has an internationally acknowledged research capability in stochastic differential equations, stochastic partial differential equations, time series, non-local operators, rough path theory and its applications in machine learning/data science. Research by the group on stochastic numerical solutions for nonlinear energy models, stochastic differential equation epidemic models and time-series models for financial data contributes across many of the University’s strategic research themes, notably Energy and Health & Wellbeing.
Group members:
- Mohammud Foondun
- Xuerong Mao (Group Lead)
- Jiazhu Pan
- Yue Wu