Senior Member: IEEE, CSI, and ACM | Life Member: ISTE, ISCA, and IETE
Laboratory for Data Science & Analytics, Department of Computer Science
South Asian University (An International University Established by all SAARC Member States)
Muhammad Abulaish is a Professor in the Department of Computer Science and Engineering at South Asian University (SAU), New Delhi, India. With more than 26 years of experience in academia, research, and academic administration, he has contributed extensively to the fields of Artificial Intelligence, Data Mining, Machine Learning, and Network Science. Since joining SAU in 2016, he has held several academic leadership positions, including Chairperson of the Department of Computer Science (2016–2018 and 2021–2023), Director (Admissions and Examinations), and Acting Registrar (2022–2024).
Prior to joining SAU, he served for over eighteen years at Jamia Millia Islamia (A Central University), New Delhi, where he held various academic and administrative responsibilities, including Professor and Head of the Department of Computer Science. He also worked at the Centre of Excellence in Information Assurance, King Saud University, Riyadh, Saudi Arabia, where he led the Internet Surveillance and Forensics Research Group.
He received his Ph.D. in Computer Science from the Indian Institute of Technology (IIT) Delhi in 2007. He is the founder of the Laboratory for Data Science and Analytics (LDSA) at SAU, an interdisciplinary research laboratory focused on data-intensive approaches for addressing real-world societal and industrial challenges.
His research interests include text analytics, social network analysis, misinformation and rumor detection, figurative language processing, sentiment and emotion analysis, health informatics, and AI-driven cybersecurity. His work emphasizes the development of intelligent data-driven methods for large-scale and complex information systems.
He has authored more than 140 research publications in leading international journals, conference proceedings, and edited volumes, including multiple papers published in IEEE and ACM Transactions. His research has appeared in several prominent venues in data mining, artificial intelligence, machine learning, and network science.
He currently serves as Associate Editor of Social Network Analysis and Mining (Springer) and Online Social Networks and Media (Elsevier). He has served as Senior Program Committee Member of ACM CIKM (2022–2024) and regularly contributes to the program committees of internationally reputed conferences, including SDM, CIKM, IJCAI-ECAI, PAKDD, ASONAM, and IEEE/WIC Web Intelligence. He has also served in organizational roles such as Publicity Co-Chair for WI’19 and WI’20, and Workshop Co-Chair for ASONAM’20.
He is a Senior Member of IEEE, ACM, and CSI, and a Life Member of ISTE, IETE, and ISCA.
conteNXt: A Graph-Based Approach to Assimilate Content and Context for Event Detection in OSNSielvie Sharma, Muhammad Abulaish, and Tanvir Ahmad, IEEE Transactions on Computational Social Systems, Volume 11, Issue 4, pp. 5483-5495, 2024.
A Multi-Task Learning Framework using Graph Attention Network for User Stance and Rumor Veracity PredictionMuhammad Abulaish, Anuj Saraswat, and Mohd Fazil, In Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Kusadasi, Turkey, 6-9 November 2023, pp. 1-5.
Domain-Specific Keyword Extraction Using Joint Modeling of Local and Global Contextual SemanticsMuhammad Abulaish, Mohd Fazil, and Muhammed J. Zaki, ACM Transactions on Knowledge Discovery from Data, Article No.: 70, 2022, pp, 1-30.
DeepSBD: A Deep Neural Network Model With Attention Mechanism for SocialBot DetectionMohd Fazil, Amit Kumar Sah, and Muhammad Abulaish, IEEE Transactions on Information Forensics and Security, Vol. 16, 2021, pp. 4211-4223.
CAT-BiGRU: Convolution and Attention with Bi-directional Gated Recurrent Unit for Self-Deprecating Sarcasm DetectionAshraf Kamal and Muhammad Abulaish, Cognitive Computation, Topical Collection: A Decade on Sentic Computing, Vol. 14, Springer, 2021, pp. 91-109.
