Dr Brian Davis

Computer Science, Hamilton Institute




I am currently a Lecturer(ATB) in Computer Science, Maynooth University, where  along with my research activities I am charged with delivering modules in Computer Science at both the undergraduate and masters levels..  Prior to taking up my appointment at Maynooth Univeristy, I was a Research Fellow, Adjunct Lecturer and Research Unit Leader at the INSIGHT Center for Data Analytics, NUI Galway (NUIG). Since June 2014, I led the Knowledge Discovery Unit (KDU, 4 PhDs, 2 MSc, 3 PostDoc, 4 RAs), focusing on the specific research areas of: Natural Language Processing, Data Visualization and Knowledge Discovery from heterogeneous data sources (text and graph). I was charged with managing the SW1 work package of the SFI Insight Grant.   In addition, I was Principle Investigator of two SFI co-funded Targeted Projects (Elsevier and DataLive, respectively) and furthermore I am Coordinator of a 3 year Horizon 2020 Innovation Action – SSIX - Social Sentiment Financial Indexes.  My core expertise intersects with Natural Language Processing, Ontology Engineering and Data Visualization.  Currently, within my unit we are exploring distributional semantic models for concept detection, relation classification and re-ranking of object classification in images  co-operation with Insight@DCU. Other research interests include: NLP for social media, cross lingual opinion mining from social media for the finance and political domains and combining visualization and textualisation using Natural Language Generation.   While I have diversified my research interests in recent years, information extraction continues to be a core interest of mine having over eight years research experience in Ontology Based Information Extraction and Semantic Annotation.  I continue to be very passionate about NLP frameworks (completing all certification exams for the GATE - General Architecture for Text Engineering framework). I am particularly interested in conducting more research into Open Architectures for Text Generation.