Dr. Muhammad Rafi, PhD

Associate Professor and Coordinator

MUHAMMAD RAFI (Member, IEEE) was born in Karachi, Pakistan. He received the B.S. and M.S. degrees from the FAST-Institute of Computer Science affiliated with the University of Karachi, Pakistan, in 1996 and 2000,respectively, all in computer science, and the Ph.D. degree in computer science, in 2017. He has more than ten years of experience in software development and also working as a Consultant for local software industry. His current research interests include algorithm development, machine learning, information retrieval, text/data mining, time series analysis, and natural language processing. He has received several travel grant awards for presenting his work at the top conferences. He has served as a Judge and a Technical Quality Review Team at many versions of IEEEXtreme Programming competitions. He has served as a reviewer for various international journals of high impact.


Courses Taught


-Data Structures


-Data Warehousing

-Data Mining

-Information Retrieval

-Information Processing Technique

-Artificial Intelligence

-Machine Learning

-Theory of Automata -I

-Theory of Automata – II

-Natural Language Processing




PhD (Computer Science)




MS (Gold Medalist)




Hanif, M.; Tahir, M.A.; Rafi, M. VRBagged-Net: Ensemble Based Deep Learning Model for Disaster Event Classification. Electronics 2021, 10, 1411. https://doi.org/10.3390/electronics10121411

Rafi, M., Wahab, M. T., Khan, M. B., & Raza, H. Towards optimal ATM cash replenishment using time series analysis, Journal of Intelligent & Fuzzy Systems, vol. 41, no. 6, pp. 5915-5927, 2021, 1-13.

Syed, F.H., Tahir, M.A., Rafi, M. et al. Feature selection for semi-supervised multi-target regression using genetic algorithm. Appl Intell (2021). https://doi.org/10.1007/s10489-021-02291-9

Butt BH, Rafi M, Sabih M. 2021. A systematic metadata harvesting workflow for analysing scientific networks. PeerJ Comput. Sci. 7:e421 DOI 10.7717/peerj-cs.421 [IF 3.09]

A. Ahmed, S. Hameed, M. Rafi and Q. K. A. Mirza, “An Intelligent and Time-Efficient DDoS Identification Framework for Real-Time Enterprise Networks: SAD-F: Spark Based Anomaly Detection Framework,” in IEEE Access, vol. 8, pp. 219483-219502, 2020, doi: 10.1109/ACCESS.2020.3042905[IF 3.745]

M. U. Ghani, M. Rafi and M. A. Tahir, “Discriminative adaptive sets for multi-label classification,” in IEEE Access, doi: 10.1109/ACCESS.2020.3041763. [IF 3.745]

Rafi M., Waqar M., Ajaz H., Ayub U. and Danish M. 2017. Document Clustering using Self-Organizing Maps. MENDEL. 23, 1 (Jun. 2017), 111-118. DOI:https://doi.org/10.13164/mendel.2017.1.111. [IF 0.20]