ADAPT Centre, School of Computing, Dublin City University
Prof. Way obtained a B.Sc. (Hons) in 1986, an M.Sc. in 1989, and his PhD in 2001 from the University of Essex, Colchester, U.K. From 1988-91 he worked at the University of Essex, U.K., on the Eurotra MT project. He joined DCU in 1991, and is currently employed as Full Professor. Recently, he was the recipient of the 2015 DCU President’s Research Award for Science and Engineering.
Prof. Way was Principal Investigator for Integrated Language Technologies in CNGL until June 2011, when he took a career break until December 2013 to work in the translation industry in the UK. Since his return to DCU in Jan. 2014, Prof. Way acted as Deputy Director of CNGL, and subsequently Deputy Director and Co-Applicant of ADAPT, the new €50 million SFI-funded Centre for Digital Content Technology. In ADAPT Prof. Way leads the Transforming Digital Content Theme as well as the Localisation Spoke, supervising projects with prominent industry partners such as Microsoft, eBay, PayPal, Huawei, and Welocalize.
Prof. Way has secured grants totalling over €64.7 million, with over €10.1 million directly for his own research. He has been lead researcher on ten EU projects bringing in over €21M. Prof. Way served as President of the European Association for Machine Translation from 2009-2015, and was President of the International Association for Machine Translation from 2011–13. He has been Editor of the Machine Translation journal since 2007. He is currently editing a series of six books for Springer in the area of Machine Translation.
Prof. Way has published over 350 peer-reviewed papers, and currently has a Google Scholar h-index of 35, an i-10 index of 118, and over 4,100 citations for his research. He has successfully graduated 23 PhD students, three of whom have won Best Thesis Awards, and 11 M.Sc. students. He currently supervises six PhD students, and 15 Postdoctoral researchers. Prof. Way’s research interests include all areas of machine translation: statistical MT, example-based MT, neural MT, rule-based MT, hybrid models of MT, MT evaluation, teaching MT, etc.