Personal profile
Professional Information
Dr M. Atif Qureshi, based in TU Dublin, is a lecturer in Data Analytics. Atif is an active funded researcher, and his research focuses on natural language processing, machine learning, explainable artificial intelligence, disinformation space, and social media analytics. Among his recent noticeable contribution, Atif has completed an exploratory search project used by medical professionals under SFI COVID-19 rapid response call. Among his research contributions, his work is referenced on Wikipedia's Word Embedding article as a pioneer contribution in the Explainable AI discourse. Before the current appointment, Atif had contributed as technical lead to EU, EI, SFI funded projects and licensed an outcome to a leading media organisation of Ireland in the space of social media analytics. Atif is passionate about applied research and has authored over 40+ peer-review research publications.
Research Interests
Data Analytics, Text Mining, Business Analytics, Knowledge Graphs
Education/Academic qualification
PhD, National University of Ireland, Galway
Award Date: 1 Sep 2015
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Collaborations and top research areas from the last five years
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Projects
- 2 Finished
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HRB Post-doctoral internship
Qureshi, M. A. (PI)
7/07/22 → 6/07/24
Project: Career Development › Recognised Researcher (HRS4R - R2)
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InEire: Towards an Inclusive Ireland through an Economic Assessment of Anti-Immigrant Sentiment in Ireland
Qureshi, M. A. (PI) & Qureshi, M. A. (CoI)
1/09/21 → 1/03/23
Project
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e-profits: a business-aligned evaluation metric for profit-sensitive customer churn prediction
Manzoor, A., Qureshi, M. A., Kidney, E. & Longo, L., Dec 2026, In: International Journal of Data Science and Analytics. 22, 1, 75.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Explainable AI for Hate Speech Moderation: A Stakeholder-Centered and Sociotechnical Review
Qureshi, M. D. M., Qureshi, M. A. & Rashwan, W., Mar 2026, In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 16, 1, e70076.Research output: Contribution to journal › Article › peer-review
Open Access -
SHAP-RC: A Framework for Explaining Annotator Disagreement in Sexism Detection
Sawant, M., Younus, A., Caton, S. & Qureshi, M. A., 2026, Explainable Artificial Intelligence - 3rd World Conference, xAI 2025, Proceedings. Guidotti, R., Schmid, U. & Longo, L. (eds.). Springer Science and Business Media Deutschland GmbH, p. 201-224 24 p. (Communications in Computer and Information Science; vol. 2580 CCIS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open AccessFile -
Explainability in Action: A Metric-Driven Assessment of Five XAI Methods for Healthcare Tabular Models
Qureshi, M. A., Noor, A. A., Manzoor, A., Qureshi, M. D. M., Younus, A. & Rashwan, W., 21 May 2025Research output: Other contribution
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Unveiling Explainable AI in Healthcare: Current Trends, Challenges, and Future Directions
Noor, A. A., Manzoor, A., Mazhar Qureshi, M. D., Qureshi, M. A. & Rashwan, W., Jun 2025, In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 15, 2, e70018.Research output: Contribution to journal › Article › peer-review
Open AccessFile