Dr Bujar Raufi
Lecturer in Business Data Analytics
01 661 0168
Block B, Merrion Centre, Merrion Road, Dublin 4
Business Data Analytics
Dr Raufi is a lecturer and learning content creator at Hibernia College. He is currently a postdoctoral researcher at TU Dublin specializing in the assessment of cognitive load in user interfaces. Bujar Raufi holds a PhD in Communications and Computer Technologies – Computer Systems, Complexes and Networks from Technical University in Sofia, Bulgaria. Bujar’s research background is in designing and implementing new approaches and directions regarding user Adaptive Intelligent Systems by fusing Data Mining and IR techniques with Semantic Web. Bujar has been awarded the Marie Curie fellowship in 2020 and he is researching in the field of Human Mental Workload and personalisation. The fellowship involves developing personalised user interfaces based on cognitive load, more precisely utilising human mental workload in generating indexes for user modelling for personalisation. Bujar also has industrial experience with Agrotics which is an SaaS-based Agrotech platform specializing in crop monitoring and yield prediction using smart technologies. Bujar’s involvement with Agrotics is a head of machine learning research. Prior to joining Hibernia College and TU Dublin, Bujar spent 17 years working as a teaching assistant at South East European University and three years on the online learning team, where he was responsible for the implementation and dissemination of the digital learning materials for various undergraduate and postgraduate courses in computer science programs.
Dr Raufi’s research interest focuses on human mental workload modelling for Human-Computer Interfaces (HCI) for personalisation purposes. Other research interests also involve machine learning, user-centric personalization and artificial intelligence with a specific focus on user activity modelling during interface interaction.
Raufi, B. and Longo, L. (2022) ‘An evaluation of the EEG alpha-to-theta and theta-to-alpha band ratios as indexes of mental workload’, Frontiers in Neuroinformatics, 16(Article 861967). doi: 10.3389/fninf.2022.861967.
Luma-Osmani, S., Ismaili, F., Raufi, B. and Zenuni, X. (2020) ‘Causal reasoning application in smart farming and ethics: a systematic review’, Annals of Emerging Technologies in Computing (AETiC), 4(4), pp.10–19.
Raufi, B. (2019) ‘Hybrid models of performance using mental workload and usability features via supervised machine learning’, in Longo, L. and Leva, M.C. (eds.) Human mental workload: models and applications, H-workload 2019. Communications in computer and information science, vol 1107. Cham: Springer, pp.136–155.
Raufi, B., Ismaili, F., J Ajdari, J. and Zenuni, X. (2019) ‘Web personalization issues in big data and semantic web: challenges and opportunities’, Turkish Journal of Electrical Engineering & Computer Sciences, 27(4), pp.2379–2394.
Raufi, B., Ismaili, F., Ajdari, J. and Zenuni, X. (2018) ‘Evaluation of machine learning techniques for hate speech detection in mobile platforms’, Communication & Cognition, 3(4), pp.73–91.