Science for AI & AI for Science
Artificial Intelligence (AI) is the design and use of computer systems to understand and generate human-level decision making. These systems represent, reason with, and learn from, different descriptions of knowledge and uncertainty. It is also the design and use of such computer systems, and their associated algorithms and architectures, to solve practical problems in different domains.
Intelligent Robotics Lab (IRLab)
The Intelligent Robotics Lab (IRLab) explores basic and applied research projects, intersecting AI, robotics, and machine learning.
Machine Learning
Generalisation analysis and uncertainty estimation
Generalisation analysis and uncertainty estimation.
Understanding the theoretical underpinning of learning methods, development and analysis of algorithms that enable valid conclusions from empirical data.
Evolutionary & Nature-inspired Computation
Runtime and dynamical analysis and multi-objective optimisation.
Development and analysis of methods inspired by natural systems such as evolution, ant foraging, molecular computation and cell signalling.
Computational Modelling
Neural Networks and Complex Systems.
Analysis of properties of complex models, development of domain-specific models in healthcare, natural language, bio-medicine, industrial automation, cyber security, finance, astronomy and environmental science.
Data, Streams and Pattern Mining
Extracting meaningful patterns, insights and relationships in large datasets.
Data mining, data streams, time series, and pattern mining study algorithms aimed at extracting meaningful patterns, trends, relationships, insights, and knowledge from possibly large, complex, or time-evolving data sets.
Planning and Multi-agent Systems
Complex decision-making and coordination challenges in dynamic and interactive environments.
Planning, Multi-agent Systems, and Game theory provide the building blocks for addressing complex decision-making and coordination challenges in dynamic, and interactive environments.
Our Team
Members of our group draw methods and inspiration from a wide range of fields, including statistics, high-dimensional probability theory, random matrix theory, stochastic processes, information geometry, dynamical systems, theoretical computer science, game theory, evolutionary biology, and the physical, chemical, and brain sciences. As AI continues to transform the world, we leverage these diverse perspectives to establish rigorous foundations in its core areas, such as:
- Machine Learning, including generalisation analysis and uncertainty estimation
Sharu Jose, Ata Kaban, Oluwole Oyebamiji
- Evolutionary & Nature-inspired Computation, covering runtime and dynamical analysis, and multi-objective optimisation
Per Kristian Lehre, Miqing Li, Jon Rowe, Peter Tino, Xin Yao
- Computational Modelling, Neural Networks, and Complex Systems
Linjiang Chen, Jens Christian Claussen, Shan He, Jian Liu, Max Little, Max Di Luca, Oluwole Oyebamiji, Peter Tino
- Data, Streams, and Pattern Mining
Huiping Chen (data privacy, pattern matching), Ata Kaban (random projections), Leandro Minku (data stream mining), Peter Tino (time series)
- Planning and Multi-agent Systems
Leonardo Stella, Jens Christian Claussen
To learn more about the exciting work we do, please visit.
Contact us
Contact us
The group has a mailing list. If you would like to join the group, please email Peter Tino or Ata Kaban outlining your interests.