Lecturer

Email: atif.qureshi@tudublin.ie

Tel: 01 220 6402

Dr M. Atif Qureshi is a lecturer in Data Analytics. Atif focuses on explainable artificial intelligence, machine learning, natural language processing, medicinal information retrieval, disinformation space, business analytics, and social media analytics. Atif has contributed to various projects funded by Science Foundation Ireland and the Irish Research Council as principal investigator and technical lead for those funded by Enterprise Ireland and the EU calls and licensed an outcome to a leading media organisation of Ireland in the space of social media analytics. Several of his works are aimed at aiding policymakers and decision-makers to make more informed choices. Atif has authored over 40+ peer-review research publications with several demonstrators showcasing the nature of applied research. One of his research is a pioneer contribution in Explainable AI discourse and Word Embedding and is cited on Wikipedia under "Word embedding" and "Explainable artificial intelligence" articles.

See full list of publication at: https://scholar.google.com/citations?hl=en&user=kVYjV4sAAAAJ&view_op=list_works

 

Current research focuses: Explainable Artificial Intelligence, Machine Learning, Business Analytics, Natural Language Processing

Academic Journal Publications 

• InÉire: An Interpretable NLP Pipeline Summarizing Inclusive Policy Making Concerning Migrants in Ireland. To view pdf: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10210388
• Qureshi, M. A., & Greene, D. (2019). Eve: explainable vector based embedding technique using wikipedia. Journal of Intelligent Information Systems, 53(1), 137-165.
• Qureshi, M. A., Younus, A., O’Riordan, C., & Pasi, G. (2018). A wikipedia-based semantic relatedness framework for effective dimensions classification in online reputation management. Journal of Ambient Intelligence and Humanized Computing, 9(5), 1403-1413.
• Miralles-Pechuán, L., Qureshi, M. A., & Namee, B. M. (2021). Real-time bidding campaigns optimization using user profile settings. Electronic Commerce Research, 1-26. 

Academic Conference Publications 

• Recommended Citation Qureshi, Muhammad Atif (2023) "ChatGPT: A tool to embrace or ban in Academia?," Irish Journal of Academic Practice: Vol. 11: Iss. 1, Article 7. Available at: https://arrow.tudublin.ie/ijap/vol11/iss1/7
• Li, W., Choudhary, R., Younus, A., Ohana, B., Baker, N., Leen, B., & Qureshi, M. A. (2021, October). RCES: Rapid Cues Exploratory Search Using Taxonomies For COVID-19. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management(pp. 4739-4743).
• Younus, A., & Qureshi, M. A. (2020, December). Combining BERT with Contextual Linguistic Features for Identification of Propaganda Spans in News Articles. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 5864-5866). IEEE.
• Qureshi, M. A., O'Riordan, C., & Pasi, G. (2012, October). Short-text domain specific key terms/phrases extraction using an n-gram model with wikipedia. In Proceedings of the 21st ACM international conference on Information and knowledge management (pp. 2515-2518).

Chapters in Books 

• Younus, A., Qureshi, M.A., Jeon, M., Kazemi, A., Caton, S. (2022). XAI Analysis of Online Activism to Capture Integration in Irish Society Through Twitter. In: Hopfgartner, F., Jaidka, K., Mayr, P., Jose, J., Breitsohl, J. (eds) Social Informatics. SocInfo 2022. Lecture Notes in Computer Science, vol 13618. Springer, Cham. https://doi.org/10.1007/978-3-031-19097-1_14
• Atif Qureshi, M., Miralles-Pechuán, L., Payne, J., O’Malley, R., Namee, B.M. (2020). Valve Health Identification Using Sensors and Machine Learning Methods. In: , et al. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. ITEM IoT Streams 2020 2020. Communications in Computer and Information Science, vol 1325. Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_4
• Younus, A., Qureshi, M.A., Manchanda, P., O’Riordan, C., Pasi, G. (2014). Utilizing Microblog Data in a Topic Modelling Framework for Scientific Articles’ Recommendation. In: Aiello, L.M., McFarland, D. (eds) Social Informatics. SocInfo 2014. Lecture Notes in Computer Science, vol 8851. Springer, Cham. https://doi.org/10.1007/978-3-319-13734-6_28
• Qureshi, M.A., O’Riordan, C., Pasi, G. (2014). Exploiting Wikipedia for Entity Name Disambiguation in Tweets. In: Métais, E., Roche, M., Teisseire, M. (eds) Natural Language Processing and Information Systems. NLDB 2014. Lecture Notes in Computer Science, vol 8455. Springer, Cham. https://doi.org/10.1007/978-3-319-07983-7_25   

Working Paper Series Arrow/TU Dublin 

• Wang, S., Qureshi, M. A., Miralles-Pechuaán, L., Huynh-The, T., Gadekallu, T. R., & Liyanage, M. (2021). Explainable AI for B5G/6G: Technical Aspects, Use Cases, and Research Challenges. arXiv preprint arXiv:2112.04698.
• Yousuf, B., Qureshi, M. A., Spillane, B., Munnelly, G., Carroll, O., Runswick, M., ... & Suiter, J. (2021). PROVENANCE: An intermediary-free solution for digital content verification. arXiv preprint arXiv:2111.08791.