To analyse the interplays between probability theory and stochastic processes, algorithmic randomness, computability, and complexity and apply them to study fine properties of algorithmic stochastic processes.
Leader: Prof S Mukeru - mukers@unisa.ac.za
Artificial intelligence (AI) is pervading many aspects of society and business with the result that individuals and businesses are becoming ever more reliant on AI to make decisions and achieve everyday tasks. Computational intelligence (CI) is a sub-field of AI that incorporates nature-inspired technologies such as neural networks and evolutionary computation that form the foundation of most recent tools in AI. The CIDS Research Group focuses on both fundamental research in CI technologies and applied research in the form of data science projects to address problems in the context of South Africa.
Leader: Prof KM Malan - malankm@unisa.ac.za
Continuous and discrete mathematical methods serve as a cornerstone for studying the problems that arise in management, physics, engineering, and other sciences. This interdisciplinary research focuses on developing new mathematical techniques associated with special functions, integrability, graph theory, approximation theory, differential equations, and nonlinear equations.
Leader: Dr S Singh - singhs2@unisa.ac.za
To provide a space for research that takes the human element into consideration.
Leader: Prof CJ Swanepoel - swanecj@unisa.ac.za
The research group in Quantitative Finance focus on studying and applying techniques from Stochastic Processes, Approximation Theory, Machine Learning, and Control Theory to better understand and address problems in the fields of Financial Derivatives Pricing, Portfolio Optimisation and Risk Management.
Leader: Prof HP Mashele - mashehp@unisa.ac.za
The Applied Mathematical Modelling and Decision Analysis Group (AMMDAG) focuses on employing a wide array of mathematical and statistical techniques to tackle complex real-world challenges. Our research encompasses the use of simulation, agent-based modelling (ABM), ordinary differential equations (ODEs), partial differential equations (PDEs), and nonstandard finite difference methods to model dynamic systems. We also utilize multivariable mathematical decision-making techniques, probability, and statistical methods, as well as stochastic control and optimization approaches. Our work spans diverse fields including public health, finance, health economics, and other critical areas, providing valuable insights and data-driven solutions for decision-making and policy development.
Leader: Prof E Mudimu - emudimu@unisa.ac.za
Last modified: 2024/08/13