Projects per year
Personal profile
Professional Information
Dr M. Atif Qureshi is a Senior Lecturer in Data Analytics at Technological University Dublin. He is an active, funded researcher whose work spans natural language processing, machine learning, explainable artificial intelligence, disinformation, and social media analytics. His recent research includes an exploratory search system developed for medical professionals under the Science Foundation Ireland COVID-19 Rapid Response Call. His work has also been referenced in Wikipedia’s article on Word Embedding in recognition of an early contribution to the discourse on explainable artificial intelligence.
Before joining TU Dublin, Dr Qureshi contributed as a technical lead to projects funded by the European Union, Enterprise Ireland, and Science Foundation Ireland. He also led work in social media analytics that resulted in the licensing of a research outcome to a leading Irish media organisation. He is particularly interested in applied and interdisciplinary research and has authored more than 40 peer-reviewed publications.
Research Interests
Explainable and Responsible Artificial Intelligence; Machine Learning; Natural Language Processing and Text Mining; Data and Business Analytics; AI Governance and Ethics; Disinformation and Social Media Analytics; Knowledge Graphs; and Applied AI in Healthcare, Business, and Public-Interest Domains.
Education/Academic qualification
PhD, National University of Ireland, Galway
Award Date: 1 Sept 2015
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Collaborations and top research areas from the last five years
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
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Explainability in action: A metric-driven assessment of local explanations for healthcare tabular models
Qureshi, M. A., Noor, A. A., Manzoor, A., Mazhar Qureshi, M. D., Younus, A. & Rashwan, W., 2026, In: PLoS ONE. 21, 7, p. e0351473 e0351473.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, 31 p., e70076.Research output: Contribution to journal › Article › peer-review
Open AccessFile -
Profit-Sensitive Benchmarking of Post-hoc Explainability in Customer Churn
Manzoor, A., Mazhar Qureshi, M. D., Yousuf, B., Kidney, E. & Atif Qureshi, M., 2026, Artificial Intelligence and Cognitive Science - 33rd Irish Conference, AICS 2025, Revised Selected Papers. Mai, T. T., Ward, T., Gurrin, C., Tran, A.L.-D., Zhang, L., Rossetto, L., Lee, H., Marks, G., Zhou, L., Jung, A. D. & Rushe, E. (eds.). Springer Science and Business Media Deutschland GmbH, p. 348-360 13 p. (Communications in Computer and Information Science; vol. 2950 CCIS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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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