Dr. Muhammad Waqas Sheikh holds a Ph.D. in Computer Science from the National University of Computer and Emerging Sciences (FAST-NUCES), Karachi, pursued under the esteemed FAST Ph.D. Fellowship Program. He also earned an M.S. degree in Computer Science from Air University, Islamabad. He is currently serving as an Assistant Professor at FAST-NUCES and has previously worked as a lecturer at Baluchistan University of IT, Engineering, and Management Sciences (BUITEM). Dr. Sheikh’s research primarily focuses on Machine Learning and Intelligent algorithms, with a specialization in Multiple-instance Learning (MIL). He has published in several high-impact factor journals and reputable conferences, making significant contributions to research and education in the field.
PhD (Computer Science)
Waqas, Muhammad, Muhammad Atif Tahir, and Rizwan Qureshi. “Deep Gaussian mixture model based instance relevance estimation for multiple instance learning applications.” Applied intelligence 53, no. 9 (2023): 10310-10325. [IF=5.3]
Waqas, Muhammad, Muhammad Atif Tahir, and Salman A. Khan. “Robust bag classification approach for multi-instance learning via subspace fuzzy clustering.” Expert Systems with Applications 214 (2023): 119113. [IF=8.66]
Hanif, Muhammad, Muhammad Waqas, Amgad Muneer, Ayed Alwadain, Muhammad Atif Tahir, and Muhammad Rafi. “DeepSDC: Deep Ensemble Learner for the Classification of Social-Media Flooding Events.” Sustainability 15, no. 7 (2023): 6049. [IF=3.9]
Waqas, Muhammad, Muhammad Atif Tahir, and Rizwan Qureshi. “Ensemble-based instance relevance estimation in multiple-instance learning.” In 2021 9th European Workshop on Visual Information Processing (EUVIP), pp. 1-6. IEEE, 2021.
Waqas, Muhammad, Zeshan Khan, Shaheer Anjum, and Muhammad Atif Tahir. “Lung-Wise Tuberculosis Analysis and Automatic CT Report Generation with Hybrid Feature and Ensemble Learning.” In CLEF (Working notes). 2020.