Dr. Muhammad Imran is a Senior Scientist and Lead of the Crisis Computing team at Qatar Computing Research Institute. Dr. Imran received his Ph.D. in computer science from the University of Trento in 2013. He then worked as a Research Scientist at QCRI from 2015-2020.
Dr. Imran has published over 80 research papers in top-tier international conferences and journals, including ACL, SIGIR, ICDM, ICWSM, and WWW. Four of his papers received the "Best Paper Award" and two "Best Paper Runner-up Award." He has been serving as a co-chair of the Social Media Studies track of the ISCRAM international conference since 2014 and has served as Program Committee for many major conferences and workshops.
Dr. Muhammed Imran’s interdisciplinary research focuses on natural language processing, social computing, computer vision, and applied machine learning. He specifically develops AI models and systems for processing big data from non-traditional data sources such as social media for social good applications. Currently, he focuses on time-critical data mining during emergency events such as natural disasters to collect, classify, extract, and summarize helpful information for humanitarian aid.
Qatar Computing Research InstituteApr 2021 - Present
Qatar Computing Research InstituteDec 2014 - Apr 2021
Qatar Computing Research InstituteApr 2013 - Dec 2014
Qatar Computing Research InstituteJun 2012 - Sep 2012
National Uniersity of Science and Technology, PakistanJul 2007 - Aug 2008
University of Trento; Trento, Italy2013
Mohammad Ali Jinnah University; Islamabad, Pakistan2007
Allama Iqbal Open University; Islamabad, Pakistan2003
Processing Social Media Messages in Mass Emergency: A Survey; ACM Computing Surveys; https://dl.acm.org/citation.cfm?id=2771588
Processing Social Media Images by Combining Human and Machine Computing During Crises; In the International Journal of Human-Computer Interaction (IJHCI); https://www.tandfonline.com/doi/abs/10.1080/10447318.2018.1427831?journa...
Humanitarian Health Computing using Artificial Intelligence and Social Media: A Narrative Literature Review; In the International Journal of Medical Informatics (IJMI); https://www.sciencedirect.com/science/article/pii/S1386505618300212
Classifying and Summarizing Information from Microblogs during Epidemics; Journal of Information Systems Frontiers; https://link.springer.com/article/10.1007/s10796-018-9844-9
Domain Adaptation with Adversarial Training and Graph Embeddings; 56th Annual Meeting of the Association for Computational Linguistics (ACL); https://www.aclweb.org/anthology/P18-1099
Identifying Sub-events and Summarizing Information during Disasters; 41st International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), https://dl.acm.org/citation.cfm?id=3210030
From Situational Awareness to Actionability: Towards Improving the Utility of Social Media Data for Crisis Response; 21st ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW); https://dl.acm.org/citation.cfm?id=3274464
Using AI and Social Media Multimodal Content for Disaster Response and Management: Opportunities, Challenges, and Future Directions. In the Information Processing and Management (IPM) journal, 2020. DOI: https://doi.org/10.1016/j.ipm.2020.102261
Non-Traditional Data Sources: Providing Insights into Sustainable Development. Communications of the ACM (CACM), 2021. DOI: https://doi.org/10.1145/3447739.
Deep Learning Benchmarks and Datasets for Social Media Image Classification for Disaster Response. In Proceedings of the IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM), 2020. DOI: https://doi.org/10.1109/ASONAM49781.2020.9381294
Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets. In Proceedings of the 28th International Conference on Computational Linguistics (COLING), 2020. DOI: https://doi.org/10.18653/v1/2020.coling-main.550
Detecting Natural Disasters, Damage, and Incidents in the Wild. In Proceedings of the 16th European Conference on Computer Vision (ECCV), 2020. DOI: https://doi.org/10.1007/978-3-030-58529-7_20
GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information. ACM SIGSPATIAL Special, May, 2020. DOI: https://doi.org/10.1145/3404111.3404114
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence. In Proceedings of the 17th International Conference on Information Systems for Crisis Response and Management (ISCRAM), Virginia, USA, 2020. DOI: https://arxiv.org/abs/2004.06675
Automatic Identification of Eyewitness Messages on Twitter During Disasters. In the Journal of Information Processing and Management (IP&M), 2020. DOI: https://doi.org/10.1016/j.ipm.2019.102107
Summarizing Situational Tweets in Crisis Scenarios: An Extractive-Abstractive Approach. In IEEE Transactions on Computational Social Systems Journal (IEEE TCSS), 2019. DOI: https://doi.org/10.1109/TCSS.2019.2937899