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Research groups

Adaptive and Cognitive Systems Lab

The Adaptive and Cognitive Systems Lab investigates architectures and frameworks for self-learning cognitive systems that are able to adapt to an evolving environment. The lab's research revolves around integrating logic based models (ontologies), probabilistic models (Bayesian networks), and system models (multi-agent systems) for simulation; semantic event analysis, situation detection and situation prediction; representation and reasoning of spatial-temporal event patterns and cognitive vision systems. The lab has a strong applied research focus and is currently engineering systems that support data fusion, data analysis and sense making in diverse application domains, currently in health, biodiversity and earth observation.

CAIR UKZN

The CAIR Node at UKZN conducts research in machine learning in general and deep learning in particular. The node focuses on deep learning neural network and Bayesian deep learning approaches to solving problems in recommender systems, natural language processing, self driving cars and image processing and computer vision. The Node investigates:

  • Deep Reinforcement Learning
  • Bayesian Deep Learning
  • Deep Neural Networks

Ethics of Artificial Intelligence

The research focus of the node is on ethics of AI and aspects of Knowledge Representation and Reasoning as it interfaces with research themes in philosophy of science. Specifically, in terms of ethics of AI, the research foci are ethics of social robotics, ethics of automated weapon systems, ethics of AI and sustainability, and fair, accountable and transparent machine learning.

INF@CAIR-UP

Prof Aurona Gerber is an Associate Professor at the Department of Informatics and Information Systems and the representative of the INF@CAIR-UP CAIR node. Information Systems (IS) as discipline study the interplay between the socio and the technical, and is therefore very well positioned to investigate the impact of rapid emerging and disruptive technologies on business and society.

Knowledge Abstraction, Representation and Reasoning

The CAIR node at Stellenbosch University conducts research in knowledge abstraction, representation and reasoning as it relates to Artificial Intelligence. Our focus is therefore on the study and use of AI techniques in problem and domain representation and algorithms, both within the host Department of Information Science and across departments and faculties at the University. At present, CAIR­-SU conducts research in the the following fields:

­- Algorithmics — the study and invention of accurate, efficient and correct algorithms;

Knowledge Representation and Reasoning

KRR is a research group based at UCT under Tommie Meyer focusing on reasoning in propositional and description logics. We conduct research on the following related aspects of knowledge representation and reasoning:

  • belief revision;
  • cognitive robotics;
  • constraint solving;
  • information integration;
  • nonmonotonic and non-classical reasoning;
  • ontology construction;
  • reasoning about actions.

Multilingual Speech Technologies

Multilingual Speech Technologies (MuST) is a research niche area affiliated with the Faculty of Engineering at North-West University. Our work is focused on the study of machine learning and statistical pattern recognition, with a strong track record of applying these in speech processing applications. We have a specific interest in deep learning techniques and their application in various domains, including speech and language processing.

The Health Architecture Laboratory

The Health Architecture Laboratory (HeAL) aims to apply and advance the state of the art in Computer Science to transform health care in Africa. The lab’s research is influencing the architecture of countrywide computer systems in a number of developing countries, including South Africa, Mozambique, Zimbabwe and Rwanda.

UP Statistics

The goal of the node is to conduct applied research in Data Science and Artificial Intelligence, with a focus on Mathematical and Computational Statistics. The Stats@CAIR-UP node focuses on capacity building in Mathematical and Computational Statistics - both in the Department of Statistics as well as with partnering PDI universities. This includes building the pipeline from an undergraduate Statistics degree into the B.Sc/B.Com honours, masters and doctoral programmes as well as postgraduate development through workshops and conference attendance.