Research Themes
Contents
Foundations, Algorithms, and Discovery
Advancements in science and philosophy are not always driven by a need, but driven by a passion to explore the unknown. Abstract systems and models can be used to explore the universe we live in and any other universes that could be. The "Foundations, Algorithms and Discovery" research cluster spans topics within pure and applied maths, statistics and computer science, that advance our knowledge of possibilities and have found applications in biology and artificial intelligence.
Mathematical Foundations
[X] Algebra [X] Graph Theory and Combinatorics [X] Information theory [ ] Logic and Reasoning [ ] Optimisation [ ] Statistical Models [X] Topology and Geometry
The Mathematical Foundations sub-cluster aims to reveal the fundamental mathematical properties and relationships that span the basis of problem solving. Research in this sub-cluster ranges from the analysis of complex interactions in semi-groups, identifications of topological relationships, investigation into relationships and interactions in graph structures, and effective representation of sequences and signals.
Artificial Intelligence Algorithms
[ ] Algebra [ ] Graph Theory and Combinatorics [X] Information theory [X] Logic and Reasoning [X] Optimisation [X] Statistical Models [ ] Topology and Geometry
The Artificial Intelligence Algorithms sub-cluster aims to analyse the
concept of machine intelligence from its foundations in logic through
to deep learning. Rather than focus on the application, this
sub-cluster focuses on topics such as the design of artificial
intelligence algorithms for effective learning, analysis of the
decision processes, and investigation into the ethics and philosophy
of artificial intelligence.
Data Science Discovery
[ ] Algebra [X] Graph Theory and Combinatorics [X] Information theory [ ] Logic and Reasoning [X] Optimisation [X] Statistical Models [ ] Topology and Geometry
The Data Science Discovery sub-cluster aims to investigate statistical models and algorithms that provide insight into the generating processes behind data and methods of modelling uncertainty in data. This sub-cluster covers topics ranging from statistical modelling, approximation of manifold structures in data, and causality analysis in relational data using graphical neural networks.
