Skip to content

Toggle service links

You are here

  1. Home
  2. Professor Dawei Song

Professor Dawei Song

Profile summary

  • Central Academic Staff
  • Professor of Computing
  • Faculty of Science, Technology, Engineering & Mathematics
  • School of Computing & Communications
  • dawei.song

Professional biography

  • 2012-present: Professor of Computing, The Open University, UK
  • 2008-2012: Professor of Computing, The Robert Gordon University, UK
  • 2005-2008: Senior Lecturer in Knowledge Media, The Open University, UK
  • 2000-2005: Senior Research Scientist, Distributed Systems Technology Centre, Australia
  • 1996-2000: Received PhD (Information Systems), The Chinese University of Hong Kong, China
  • 1993-1996: Received MEng. (Computer Science), Tianjin University, China
  • 1989-1993: Received BEng. (Computer Science), Jilin University of Technology, China

Research interests

  • Information Retrieval
  • Natural Language Processing
  • Artificial Intelligence
  • Human Computer Interaction

Recent Projects

  • 1/2017 - 12/2020: H2020 Innovative Training Network: Quantum Information Access and Retrieval Theory (QUARTZ). In partnership with University of Padua (Italy) and other 5 universities. Total funding: Euro €3.46M.
  • 2/2011 - 2/2013:  Royal Society of Edinburgh (RSE) and Natural Science Foundation of China (NSFC) international joint project: Towards a Context-sensitive High-order Language Model for Information Retrieval via Information Geometry. In partnership with Tianjin University (China). Total funding: GBP£23.8K.
  • 9/2010 - 8/2013:  EU FP7 Marie-Curie IRSES project: Quantum Contextual Information Access and Retrieval (QONTEXT). In partnership with University of Glasgow, University of Padua (Italy), Brussels Free University (Belgium), Queensland University of Technology (Australia), University of Montreal (Canada), Tianjin University (China), total funding: Euro €200.8K          
  • 12/2008 - 05/2012:  EPSRC Project: Automatic Adaptation of Knowledge Structures for Assisted Information Seeking (AutoAdapt). In partnership with University of Essex. Total funding: £609K.
  • 5/2008 - 10/2011:  EPSRC Project: Towards Context-Sensitive Information Retrieval Based on Quantum Theory: With Applications to Cross-media Search and Structured Document Access (Renaissance). In partnership with University of Glasgow and Queen Mary University of London. Total funding: GBP £961.8K.
  • 11/2006 -11/2008: EPSRC Project: Operationalizing the Logical Uncertainty Principle in a Language Modelling Framework for Context-based Information Retrieval (LUPMIR). Total funding: GBP £163.4K.

Research Activity

Externally funded projects

QUantum information Access and Retrieval Theory

RoleStart dateEnd dateFunding source
Lead01/Jan/201731/Dec/2020EC (European Commission): FP(inc.Horizon2020, H2020, ERC)
We aim to establish an European Training Network (ETN) on QUantum information Access and Retrieval Theory (QUARTZ). Towards a new approach to IAR addressing the challenges of the dynamic and multimodal nature of the data and user interaction context, QUARTZ aims to educate its Early Stage Researchers (ESRs) to adopt a novel theoretically and empirically motivated approach to multimodal and multimedia IAR based on the quantum mechanical framework that gives up the notions of unimodal features and classical ranking models disconnected from context. Each ESR will be aware that the current state of the art of IAR is not sufficient to address the challenges of a dynamic, adaptive and context-aware user-machine interaction and to make a major breakthrough in the overall effectiveness of retrieval systems, and that a genuine theoretical breakthrough is on the contrary necessary. We believe that this breakthrough can be provided by quantum theory which can integrate abstract vector spaces, probability spaces and languages in a single theoretical framework which extend and generalize the classical vector, probability and languages utilised in IAR. QUARTZ will consist of training activities and ESR research projects which investigate theoretical issues and evaluate methods and prototypes for adaptive IAR systems managing large data collections and meeting the end user's information needs in a dynamic context.