Tungsten Centre for Intelligent Data Analytics

Staff

Director: Prof. Mark Bishop

Director of Research: Dr. Sebastian Danicic

Director of Operations: Dr. John Howroyd

Research Associate: Dr. Valeriia Haberland

Research Associate: Dr. Minlue Wang

Research Associate: Dr. Yun Zhou

Centre Administrator: Andrew Martin

Contact Details

Tungsten Centre for Intelligent Data Analytics
Saint James' Hall Block 1/B
Goldsmiths,
University of London,
New Cross
London,
SE14 6NW
United Kingdom

Telephone: +44 (0)20 7896 2603
Email: Andrew Martin

Appointments

Please email us if you would like to see us in our office!

The Team

Our team has strong expertise in many aspects of Mathematics, Computer Science and Artificial Intelligence. We have already had considerable success in working together as a team developing both new research ideas and deliverables to customers.

Projects

Financial Data Analytics

We have just started a new commercial research project working with our partners, Tungsten to develop a fully functional state-of-the art spend analytics system.

Research

Semantics

At the heart of spend analysis is the general problem of forming an accurate, detailed semantic understanding of items from the raw text information that is available to the system (e.g. product descriptions). This data must be analysed using the existing knowledge base; there may, however, sometimes not be enough current `context' to unambiguously `understand' this data; in such circumstances it may be necessary to enrich information via additional user interaction and/or web spidering. To help solve such semantic issues there is scope for application of new AI techniques; for example, deep learning and reservoir computing and the newly emerging area of quantum linguistics. Learning algorithms for classification based on clean data Access to our partner's massive database opens up new opportunities to research state-of-art machine learning techniques (e.g. deep learning reservoir computing; echo-state networks) which potentially could also offer a significant improvement in classification performance.

Automatic ontology generation

Ontologies are structural frameworks for organising information and are used in artificial intelligence as a form of knowledge representation about the world (or some part of it). An ontology formally represents knowledge as a set of concepts within a domain, using a shared vocabulary to denote the types, properties and interrelationships of those concepts. Automatically developing contextually sensitive ontologies will significantly improve the classification system.

Trend analysis: prediction of future price fluctuations

To explore our partner's database to identify economic trends in purchasing via the application of advanced machine learning techniques; the expectation is that with access our partner's huge database, new learning algorithms could be trained to make commercially useful time-series predictions (e.g. to highlight strategic opportunities for investment etc.).