Muhammad Fayyaz

Assistant Professor
  • Department of Computer Science
  • (041) 111-128-128
  • Ext: 124


  • Experience:
  • Interests:
  • Date of Joining:
  • Number of Students Supervised (BS/MS/PhD):
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  • Google Scholar Citations:
  • 13
  • Computer Vision, Image Analytics, NLP, Machine/Deep Learning
  • 21/01/2022
  • 4
  • 4
  • 66

Dr. Muhammad Fayyaz is serving as an Assistant Professor, at the Department of Computer Science, FAST – National University of Computer and Emerging Sciences (NUCES), Chiniot-Faisalabad Campus, Pakistan. His responsibilities include teaching at undergraduate/graduate level, research/creative work, thesis/project supervision/evaluation, student’s guidance/counseling, administrative activities, and co/extra-curricular activities at university level. Before this, he worked as Lecturer at the Department of Computer Science COMSATS University Islamabad (CUI), Wah Campus, Pakistan, from September 2014 to January 2022. Moreover, he served as SST (Computer Science) in Punjab Education Department, Sargodha District from September 2009 to August 2014. During stay at CUI, he has been fulfilling several academic and administrative duties such as member of various administrative committees (proposal/FYP project internal evaluation, scrutiny, Visio Spark, COVID rapid action force, open house & job fair), FYP undergraduate head (CS), responsible/member of session management committee (FIT Conference) and many other events, along with teaching and research. His overall job experience both at undergraduate and graduate levels in teaching, as well as research, pertains to 13 years.

Dr. Muhammad Fayyaz received his Ph.D. in Computer Science in September 2021 from COMSAT University Islamabad, Pakistan. He remained a very active research member of fields related to Pedestrian Analysis, Computer Vision and Pattern Recognition. Overall, he has published high impact factor papers in journals of international repute with cumulative impact factor 25+. His areas of interest are digital image processing, machine/deep learning, computer vision, and natural language processing. He has supervised/co-supervised 4 MSCS students and more than 25 R&D projects of BSCS/BSSE/MCS students. He is reviewer of several prestigious journals and conferences. Along with teaching and research activities, he remained active member of administrative and managerial committees. He is active member of research group named computer vision and intelligent system (CVIS). His objectives are (1) to improve students’ behavior and to instill students with intrinsic motivation to learn, and (2) to pursue career in a progressive organization that opens new vistas of broadening knowledge and pursuing the zenith of professionalism in teaching and research.


  • Ph.D. (Computer Science), COMSATS University Islamabad, Pakistan (2021)
  • Masters (Education), University of Sargodha, Sargodha (2015) [Gold Medalist]
  • MS (Computer Science), University of Sargodha, Sargodha (2014) [Second Position]
  • Bachelors (Education), University of Sargodha, Sargodha (2012)
  • Masters (Computer Science), University of Sargodha, Sargodha (2007)
  • Bachelor (Computer Science), AIOU, Islamabad (2003)


Journals Publications

1. Muhammad Fayyaz, Mussarat Yasmin, Muhammad Sharif, Tasswar Iqbal, Mudassar Raza, Muhammad Imran Babar. “Pedestrian gender classification on imbalanced and small datasets using fusion of deep and traditional features.” Neural Computing and Applications (2023). [IF 5.102, Q1]
2. Marryam Murtaza, Muhammad Sharif, Mussarat Yasmin, Muhammad Fayyaz, Seifedine Kadry, Mi Young Lee “Clothes Retrieval using M-AlexNet with Mish Function and Feature Selection using Joint Shannon’s Entropy Pearson’s Correlation Coefficient (JSE-PCC)” IEEE Access 2022. [IF 3.476, Q1]
3. Farhat Abbas, Mussarat Yasmin, Muhammad Fayyaz, Mohamed Abd Elaziz*, Songfeng Lu*, Ahmed A. Abd El-Latif. “Gender Classification using Proposed CNN-based Model and Ant Colony Optimization.” Mathematics (2021). [IF 2.592, Q2]
4. Mehshan Ahad*, Muhammad Fayyaz. “Pedestrian gender recognition with handcrafted features ensembles.” Azerbaijan journal of high performance computing, Volume 4 (1), June 2021, Pages 60-90.
5. Muhammad Fayyaz*, Mussarat Yasmin, Muhammad Sharif, and Mudassar Raza. “J-LDFR: joint low-level and deep neural network feature representations for pedestrian gender classification.” Neural Computing and Applications 33 (2021): 361-391. [IF 5.102, Q1]
6. Muhammad Pervez Akhtar*, Zheng Jiangbin, Irfan Raza Naqvi, Mohammed Abdelmajeed, and Muhammad Fayyaz. “Exploring deep learning approaches for Urdu text classification in product manufacturing.” Enterprise Information Systems: volume 16, issue 2, 2020. [IF 4.407, Q1]
7. Muhammad Fayyaz, Mussarat Yasmin, Muhammad Sharif, Jamal Hussain Shah*, Mudassar Raza, and Tassawar Iqbal. “Person re-identification with features-based clustering and deep features.” Neural Computing and Applications 32, no. 14 (2020): 10519-10540. [IF 5.102, Q1]

Journal Submissions

1. Mian Muhammad Talha, Hikmat Ullah Khan, Muhammad Hamza, Ammar Saeed, Muhammad Fayyaz. “News Recommendations Based on Click Rate Feature Matrix Using Deep Learning Models” Online Information Review 2023. [Under Review]
2. Mian Muhammad Talha, Hikmat Ullah Khan, Saqib Iqbal, Mohammed Alghobiri, Tassawar Iqbal, Muhammad Fayyaz. “Handshake of Deep Learning with News Recommender Systems: Survey and Path ahead” Neurocomputing 2022. [Revision Submitted]
3. Farhat Abbas, Mussarat Yasmin, Muhammad Fayyaz, Usman Asim. “PGC-ViT: Pedestrian Gender Classification using Advanced Vision Transformer on Small Size Datasets” Pattern Analysis and Applications 2022. [Received First Revision, Working on Revision Notes]
4. Muhammad Ramzan, Adnan Abid, Shahid Mahmood Awan and Muhammad Fayyaz” A novel hybrid approach for efficient driver drowsiness detection using custom deep learning models.” Neural Computing and Applications 2022. [Revision Submitted]

Collaborations at National and International Level

[International Level]

He is doing collaborative research work with Dr. Bilel Benjdira, Research Assistant at Prince Sultan University, KSA. They both are focusing on deep learning-based models for tasks related to medical imaging such as the classification of knee osteoarthritis severity grading and COVID prediction from x-ray images and vision transformers for pedestrian image analysis.

[National Level]

He is doing collaborative research work with Dr. Tehseen Zia, Assistant Professor, COMSATS University Islamabad (CUI), Pakistan. In this collaborative work, they are investigating patch-by-patch interpretable medical images to report generation using report grounding. This work will be generally applicable in enhancing the diagnostic performance and generality of report-generating models and enabling the integration of these systems into clinical workflows.
He is also in contact with Dr. Mussarat Yasmin, Dr. Hikmat Ullah Khan, and Dr. Tassawar Iqbal Associate Professors at the Department of Computer Science, CUI, Pakistan for collaborative research work which covers different research areas such as image analytics, information retrieval, and social data analytics.

Detail of Funded Projects

  1. At undergraduate level, he has worked on National Grassroot ICT Research Initiative (NGIRI) funded project named Water and Land Garbage Cleaner, at CUI, Wah Campus, Pakistan, during 2017-18, Amount 28,650/- PKR [Completed]
  2. Research project Title ”Pose-Based Pedestrian Orientation Analysis Using Machine/Deep Learning Techniques” funded by ORIC FAST NUCES, Pakistan, (2023-24). Amount 7,00,000/- PKR [In Progress]
  1. Research project Title ” Assistive Technology for Elderly People with Dementia” funded by ORIC FAST NUCES, Pakistan, (2023-24). Amount 9,00,000 PKR/- PKR [In Progress]