School of Computing

Data Science Research Group - Research Expertise

Shoaib Jameel's research interest mainly lies in text processing using machine learning models to obtain meaningful information from large-scale text data. He has worked with the leading universities and research institutes across the globe. His research has also featured on the front page of newspapers. All his works primarily revolve around proposing novel computational methods for machine learning with applications to text mining. Specifically, his work has centered around learning low-dimensional representations of natural language text on a large-scale. Among others, he has developed a variety of probabilistic topic models, which have seen applications in text mining and information retrieval, as well as vector space embeddings, which have shown promising results in tasks such as knowledge base completion and commonsense reasoning.

Huy Phan investigates topics in the fields of machine learning/deep learning and signal processing. He is particularly interested in speech analysis, environmental sound analysis, and biosignal analysis, and healthcare applications.

Anna Jordanous's research areas include computational creativity and its evaluation, music informatics, digital humanities, knowledge modelling, Semantic Web, and natural language processing. Primarily she works with computational creativity - the modelling, simulation or replication of creative activities and behaviour using computational means - with a focus on the question of how to evaluate claims of computer software being creative. As well as writing creative software to improvise music, Dr. Jordanous has contributed a highly-cited standardised procedure for evaluating creative systems. She also uses music information retrieval and natural language processing in her work.

Michael Kampouridis is interested in the use of computational intelligence techniques in business-related problems. He has successfully used Evolutionary Algorithms and Artificial Neural Networks in the fields of finance, economics, and telecommunications.

Professor Ian McLoughlin's research revolves around speech and audio signal processing, hearing and communications and embedded systems. He make tiny devices that monitor the world around them as well as systems that process and improve speech communications. Many of those systems employ advanced machine learning techniques as well as complex signal processing methodologies. This work has impact among speech loss patients, emergency communications systems, smart homes of the future and mobile device interaction.

Palani Ramaswamy's research area is on biological signal processing, mainly signals from the brain and heart. He studies these signals (such as EEG, PCG and ECG) for several applications: brain-computer interface, biometrics, electrophysiological analysis, cardiovascular disease diagnosis and stress management. Tools like advanced signal processing and machine learning (such as neural networks and genetic algorithms) are utilised. Further, he also process signals for various engineering and computer science applications.

Professor Alex Freitas's research interests involve the following areas: Data Mining and Knowledge Discovery, focusing on developing new classification methods that produce interpretable models (e.g., decision trees, if-then rules and Bayesian network classifiers); Applications of classification methods in the Biology of Ageing, Protein Function Prediction, and Pharmacokinetics; Biologically-inspired algorithms: mainly Evolutionary Algorithms and Ant Colony Optimisation.

Caroline Li's main area of research is in signal processing and its applications in body sensors, including: EEG-based biomarker discovery for brain diseases, neurofeedback applications for medical and sport applications and brain computer interface. Also she is working on signal processing methods such as adaptive filtering, tracking methods and machine learning methods for pattern classification.

Professor Howard Bowman is interested in how the mind emerges from the brain to generate a spectrum of cognitive capacities. In this respect, he undertakes work focusing on the following capacities: perception, consciousness, attention, language, emotions and decision-making. He studies these topics using a mixture of methods, which includes behavioural and electrophysiological (EEG) experimentation and connectionist and symbolic modelling. Study of these topics is especially timely, since modern brain imaging techniques are beginning to reveal the physical mechanisms from which cognition emerges, thus, enabling biologically plausible models of cognition to be constructed. In this area he is currently working on the following topics: emotions, salience sensitive control of human attention and computational modelling; reinforcement learning investigations of human decision making; using neural networks to model how subliminal visual stimuli can initiate motor responses; and formal methods in HCI and cognition.

Fernando Otero’s research interests include Data Mining and Knowledge Discovery, in particular classification and regression – focusing on the creation of interpretable models – and more recently clustering. He also works on bio-inspired algorithms, mainly ant colony optimization and genetic programming for applications in bioinformatics (e.g., protein function prediction) and financial forecasting and large-scale data mining ("Big Data").

Matteo Migliavacca works in networked systems, with an emphasis on system building and evaluation. His research interests include parallel data processing, including stream processing and publish subscribe middleware in large scale and cloud scenarios. Lately he has been involved in security, including secure event processing, runtime taint tracking and information flow control in a variety of languages from Java, to PHP and Erlang. He is currently collaborating with the LSDS group at Imperial College on Stateful Big Data processing (SEEP project) and Network as a Service (NaaS project) also in collaboration with Microsoft Research.

Professor Frank Wang's research interest includes Future Computing, Green Computing (via memristor), Grid/Cloud Computing, Biologically-inspired Computing, Quantum Computing/Communication, Data Storage & Data Communication, and Data Mining and Data Warehousing.


School of Computing, University of Kent, Canterbury, Kent, CT2 7NF

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Last Updated: 09/04/2019