Data Science and Artificial Intelligence research group
The Data Science and Artificial Intelligence Research Group develops innovative methods and systems for the computational analysis of data. Our work revolves around harnessing the power of metadata, observations, and measurements from large-scale information systems to establish testable theories, conduct impactful experiments, and drive practical applications that result in tangible economic and societal benefits. Our key research areas include but not limited to:
Machine Learning: Rapid advancements in novel architectures for natural language processing, computer vision, advanced deep learning, and ground-breaking reinforcement learning. Addressing challenges in interpretability (XAI) and ethical considerations for accountable AI systems.
Big Data: Exploring methodologies to effectively process, analyse, and extract valuable insights from vast and complex datasets. Leveraging machine learning for scalable models and distributed computing systems to ensure high-performance analysis. Our work spans diverse domains, enabling data-driven decision-making and valuable predictive insights in areas like healthcare, finance, marketing, and scientific research.
Natural Language Processing: Refining algorithms for machines to understand, interpret, and generate human language. Our research delves into language translation, sentiment analysis, text summarisation, and generation. We focus on creating robust and scalable machine learning architectures, incorporating deep learning, recurrent neural networks, transformers, and attention mechanisms to tackle the challenges of language understanding and generation.
Data-driven Cyber Security: Utilising diverse data sources like network logs, user behaviour, and threat intelligence to enhance security posture and effectively detect and respond to cyber threats. Our advanced data analytics, machine learning, and artificial intelligence techniques enable proactive identification of potential breaches, predicting emerging threats, and automating incident response processes, ultimately strengthening cybersecurity resilience.
Pervasive Computing and Internet of Things: Creating an interconnected network of smart devices seamlessly integrating into daily life, forming an ambient computing environment. By harnessing data from various IoT devices, we design efficient algorithms, protocols, and architectures to enhance user experiences, optimise resource utilisation, and address privacy and security challenges.
Bioinformatics: Developing and applying advanced computational techniques and machine learning algorithms to analyse and interpret biological data, such as DNA sequences, protein structures, and gene expression patterns. Our researchers identify patterns, predict biological functions, classify diseases, and discover potential drug candidates, facilitating significant advancements in genomics, proteomics, and personalised medicine for more effective healthcare solutions.
Group members
- 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. Paul's DBLP profile. Pauls' Google Scholar profile.
- 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
- 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. Swati's DBLP profile. Swati's Google Scholar profile.
- 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. George Magoulas' DBLP profile. George Magoulas' Google Scholar profile.
- Paul Nulty. Research interests: Natural language processing, distributional semantics, digital humanities, computational social science, visualisation of linguistic data. Paul's DBLP profile. Paul's Google Scholar profile.
- Alessandro Provetti. Research interests: Experimental algorithmics, data mining, declarative programming. Alessandro's DBLP profile. Alessandro's Google Scholar profile.
- George Roussos. Research interests: Social and pervasive computing, human dynamics, infrastructure services for the Internet of Things. George Roussos' DBLP profile. George Roussos' Google Scholar profile.
- Stelios Sotiriadis. Research interests: Distributed computing systems, large scale resource management, cloud computing and big data processing. Stelios' DBLP profile. Stelios' Google Scholar profile.
- Cen Wan. Research interests: Machine learning, data mining, bioinformatics, computational biology. Cen's DBLP profile. Cen's Google Scholar profile.
- David Weston. Research interests: Statistical analysis for cell biology. David's DBLP profile.