Artificial Intelligence and data science
We develop and apply advanced methodologies in machine learning, big data, and natural language processing (NLP) to address key research challenges and deliver impactful machine intelligence solutions. Our work spans foundational theories, practical applications, and interdisciplinary collaborations, with significant contributions to critical areas like cyber security, IoT, bioinformatics, and psychology.
The AI and Data Science theme has three main groups of expertise.
Machine Learning Group
We focus on developing novel approaches in machine learning, time series and graph modelling, and large-scale image and video analysis. Our work addresses complex challenges in psychophysiological data modelling, intelligent systems, and AI in education, including the transformative impact of digital technologies and AI in the way people learn, work and communicate. Key contributions include the development of advanced cyber defence methodologies and innovative approaches to network evolution and trust in financial systems, alongside statistical and intelligent methods for bioinformatics applications.
Group members
- Anthony Brooms. Research interests: Uncertainty quantification, forward uncertainty propagation analysis for line-of-sight reconstruction problems, converging beam triple LIDAR, non-cooperative games and stochastic service systems; non-globally uniformizable stochastic systems.
- Taolue Chen. Research interests: AI (machine learning and natural language processing) for software sciences, neuro-symbolic software engineering, program analysis and verification. . .
- George Magoulas. Research interests: Computational models of learning and cognition, artificial neural networks and deep learning, evolutionary computing, learning technologies, bio-inspired machine learning, software engineering for AI and machine learning systems. . .
- Paul Nulty. Research interests: Natural language processing, distributional semantics, digital humanities, computational social science, visualisation of linguistic data. . .
- Alessandro Provetti. Research interests: Experimental algorithmics, data mining, declarative programming. . .
- Cen Wan. Research interests: Machine learning, data mining, bioinformatics, computational biology. . .
- David Weston. Research interests: Statistical analysis for cell biology. .
- Manni Singh: David Weston’s PhD student
- Dima Bsata: Paul Nulty’s PhD student
- Shibu Kurian: Paul Nulty’s PhD student
- Dimitar Atanasov: Taolue Chen’s PhD student.
Big Data Group
Our research investigates cutting-edge NLP techniques, semantic analysis, big data processing, and IoT infrastructures. We address the complexities of language evolution, human-computer interactions, and large-scale distributed systems. Our expertise includes optimising cloud computing for efficient resource management, analysing semantic networks, and exploring the pervasive role of IoT in human behaviour. We also focus on the interdisciplinary applications of NLP, integrating it with areas like digital humanities, social science, and healthcare, to advance understanding and create impactful, cross-domain solutions.
Group members
- George Roussos. Research interests: Social and pervasive computing, human dynamics, infrastructure services for the Internet of Things. . .
- Stelios Sotiriadis. Research interests: Distributed computing systems, large scale resource management, cloud computing and big data processing. . .
- Paul Yoo (Group Lead). Research interests: Application of machine learning and big data technologies in security and defence, finance and the engineering industry, theoretical and methodological problems. . .
- Dimitris Kargatzis: Stelios Sotiriadis’ PhD student
- Roderick Timmerman: Stelios Sotiriadis’ PhD student
Probability and Statistics Group
We tackle fundamental challenges in data science, leveraging expertise in uncertainty quantification, stochastic systems, and statistical methodology, applied to a variety of applications. Our work develops robust theory and methods, building on classical mathematical and statistical tools, suitable for end-use environments involving complex dependent data in either time and/or space, and inverse measurement problems. This is crucial to addressing some of the key challenges arising in practical applications ranging from medicine to the social sciences, and from statistical engineering to financial risk management. Application areas include data arising from social networks, sensors, and financial risk management.
Group members
- Swati Chandna. Research interests: Statistical analysis of network data, time series in the frequency domain, speech signal processing, bootstrap methods for time series, spatio-temporal analysis. . .
- Richard Pymar. Research interests: Probability theory; especially interacting particle systems, mixing times, and the parabolic Anderson mode. Richard's DBLP profile.
- David Weston. Research interests: Statistical analysis for cell biology. .
- Udbhav Dalavia: Swati Chandna’s PhD student
- Maryam Saghi: Richard Pymar’s PhD student