Projects per year
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Professional Information
Bujar Raufi is an assistant lecturer and a researcher at TU Dublin specializing in assessing cognitive load in user interfaces.
He holds a PhD in Communications and Computer Technologies – Computer Systems, Complexes and Networks from Technical University in Sofia, Bulgaria. His 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. He was awarded a Marie Curie fellowship in 2020 and is researching the field of Human Mental Workload and personalization. The fellowship involves developing personalized user interfaces based on cognitive load, more precisely utilizing human mental workload to generate indexes for user modelling for personalisation.
Before joining TU Dublin, he spent seventeen years as a teaching assistant at South East European University and three years on the online learning team, where he was responsible for implementing and disseminating Digital Learning materials for various undergraduate and postgraduate courses in computer science programs.
Research Interests
Mental Workload, Machine Learning, Adaptive and Personalized User Interfaces
Education/Academic qualification
PhD, Development and Implementation of Adaptive Web-Based Systems Using Data Mining Techniques and Semantic Web, Технически университет - София
1 Nov 2006 → 8 Sept 2011
Award Date: 8 Sept 2011
Master, Development and Implementation of Online Examination System, New Bulgarian University
1 Sept 2004 → 27 Sept 2006
Award Date: 27 Sept 2006
Bachelor, Text-To-Speech Synthesis for Albanian Language, South East European University
4 Sept 2001 → 27 Nov 2003
Award Date: 27 Nov 2003
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Projects
- 1 Finished
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Enabling Human Mental Workload in User Modelling for PersOnalized Web ExpeRiences
Raufi, B. (PI) & Longo, L. (CoI)
1/11/20 → 31/10/23
Project: Career Development › Recognised Researcher (HRS4R - R2)
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Explainable EEG Microstate Classification for Cognitive Workload Estimation: A Multi-method XAI Analysis Using SHAP, LIME, DiCE, and Anchors
Raufi, B., 2027, Artificial Intelligence Applications and Innovations - 22nd IFIP WG 12.5 International Conference, AIAI 2026, Proceedings. Maglogiannis, I., Iliadis, L., Papaleonidas, A. & Zervakis, M. (eds.). Springer Science and Business Media Deutschland GmbH, p. 283-298 16 p. (IFIP Advances in Information and Communication Technology; vol. 795 IFIPAICT).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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EEG Slope Entropy and Affective Self-Report Fusion for Cognitive Workload Classification: A Multi-Stage Pipeline with Explainable AI Evaluation
Kouka, M. & Raufi, B., Aug 2026, In: BioMedInformatics. 6, 4, 38.Research output: Contribution to journal › Article › peer-review
Open Access -
Convolutional Autoencoder-Based Dimensionality Reduction for EEG Microstate Analysis
Thukral, S., Raufi, B. & Božic, B., 2025, Artificial Intelligence Applications and Innovations - 21st IFIP WG 12.5 International Conference, AIAI 2025, Proceedings. Maglogiannis, I., Iliadis, L., Papaleonidas, A. & Andreou, A. (eds.). Springer Science and Business Media Deutschland GmbH, p. 69-82 14 p. (IFIP Advances in Information and Communication Technology; vol. 758 IFIPAICT).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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A Comparative Analysis of SHAP, LIME, ANCHORS, and DICE for Interpreting a Dense Neural Network in Credit Card Fraud Detection
Raufi, B., Finnegan, C. & Longo, L., 2024, Explainable Artificial Intelligence - Second World Conference, xAI 2024, Proceedings. Longo, L., Lapuschkin, S. & Seifert, C. (eds.). Springer Science and Business Media Deutschland GmbH, p. 365-383 19 p. (Communications in Computer and Information Science; vol. 2156 CCIS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Comparing ANOVA and PowerShap Feature Selection Methods via Shapley Additive Explanations of Models of Mental Workload Built with the Theta and Alpha EEG Band Ratios
Raufi, B. & Longo, L., Mar 2024, In: BioMedInformatics. 4, 1, p. 853-876 24 p.Research output: Contribution to journal › Article › peer-review
Open Access