The focus is on graph theory, specifically domination,
independence and irredundance in graphs.
Fluid flow modelling
This field concentrates on the analytical and
numerical modelling of various aspects of single and multiphase transport of
Newtonian and non-Newtonian fluids in different types of porous media.
Applications include water seepage through rocks and soils, sediment transport
and filtration processes. Various projects on the analysis of extreme events
and time series analysis in the coastal zone are conducted.
Numerical methods and computation
Algorithms for scientific computing are developed and
analysed. These algorithms include methods for solving differential equations
and performing matrix computations. These algorithms are fundamental to just
about every conceivable scientific application. Specific areas
of interest include the
computation of integral transforms and special functions, spectral methods for differential equations,
and the computation of wave phenomena. Another area of interest is the numerical modelling of mechanical aspects of biological materials.
applied machine learning
The study, development and implementation of machine learning techniques for various applications in computer vision, image processing, robotics, biometric recognition systems, time series analysis and predictive modelling.
Research on Markov processes for modelling physical systems and on numerical methods for simulating these systems, with a focus on rare or extreme events that arise with a very low probability or frequency.
This area deals with complex deterministic nonlinear dynamics (e.g. deterministric chaos) and stochastic dynamics. When components of a system can take independent decisions, their dynamics can be modelled using game theory. Large systems are represented as complex networks of interactions. The structure and dynamics of such networks can describe a large variety of systems ranging from biology, engineering, sociology, and economics.