Dr Saad Aslam

Dr Saad Aslam

  • Head​
Department of Smart Computing and Cyber Resilience
Google Scholar
SDGs Focus
Affordable and Clean Energy
Industry, Innovation and Infrastructure
Sustainable Cities and Communities

Biography

Dr. Saad Aslam is an accomplished academic and researcher with over fourteen years of experience. With a robust background in teaching at both undergraduate and graduate levels, Dr. Saad plans to elevate academic excellence by revising and introducing courses that reflect cutting-edge advancements in various computing and engineering disciplines. Dr. Saad spearheaded the design and development of new courses and curricula, successfully navigating the approval process with standardization and academic bodies. Simultaneously, his research focus on wireless communication, cellular networks, machine learning, green machine learning, and smart systems has been propelled through publications and international collaborations. In this process, Dr. Saad has produced several peer-reviewed journal articles as well as conference papers. His publications have received research awards as well. He is currently reviewing numerous academic journals as well as research proposals submitted to various international universities. Leveraging industry exposure, Dr. Saad seeks to integrate practical knowledge into academia, fostering a bridge between theory and real-world applications. 
During his career, Dr Saad Aslam has been a recipient of the best Lecturer award, teaching excellence award, student appreciation of teaching award, and honorariums.
 

Academic & Professional Qualifications

  • PhD in Electrical and Electronics Engineering, Massey University, New Zealand

Research Interests

  • Machine Learning
  • Green Machine Learning
  • Device-to-Device Communication
  • Wireless Networks / Wireless Communication/ Mobile Cellular Networks
  • Clustering Algorithms & Optimization
  • Distributed Systems
  • Smart Energy-Efficient Buildings

Teaching Areas

  • Machine Learning/ Deep Learning
  • Networks
  • Computer Science
  • Electrical and Electronic Engineering

Courses Taught

  • Computer Networks
  • Advanced Computer Networks
  • Cellular Networks
  • Communication Systems
  • Deep Learning for Data Science
  • Machine Learning
  • Analog Electronics
  • Signal and Systems
  • Digital Signal Processing

Notable Publications

  1. Hassan, B., Baig, S., Aslam, S., Asif, H. M., & Mumtaz, S. (2024). Adaptive refined random orthogonal matching pursuit algorithm for FBMC/OQAM MIMO framework. Alexandria Engineering Journal, 87, 319-328. 
  2. Arshad, Rabia, Sobia Baig, and Saad Aslam. "User clustering in cell-free massive MIMO NOMA system: A learning based and user centric approach." Alexandria Engineering Journal 90 (2024): 183-196.
  3. Aslam, S., Pajooh, H. H., Nadeem, M., & Alam, F. (2024). Green machine learning for cellular networks. In Green Machine Learning Protocols for Future Communication Networks (pp. 1-14). CRC Press.
  4. Hassan, B.; Baig, S.; Aslam, S. On Scalability of FDD-Based Cell-Free Massive MIMO Framework. Sensors 2023, 23, 6991.  
  5. Jahangeer, A., Bazai, S. U., Aslam, S., Marjan, S., Anas, M., & Hashemi, S. H. (2023). A Review on the Security of IoT Networks: From Network Layer’s Perspective. IEEE Access.
  6. Aslam, Saad, Muhammad Harris, and Salman Siddiq. "D2D Communication Underlaying UAV-Enabled Network: A Content-Sharing Perspective." Inventions 8.1 (2022): 5.
  7. Pajooh, H. H., Demidenko, S., Aslam, S., & Harris, M. (2022). Blockchain and 6G-Enabled IoT. Inventions, 7(4), 109. 
  8. Rehmani, M. A. A., Aslam, S., Tito, S. R., Soltic, S., Nieuwoudt, P., Pandey, N., & Ahmed, M. D. (2021). Power profile and thresholding assisted Multi-Label NILM classification. Energies, 14(22), 7609.
  9. Aslam, S., Alam, F., Hasan, S. F., & Rashid, M. A. (2021). A machine learning approach to enhance the performance of D2D-enabled clustered networks. IEEE Access, 9, 16114-16132.
  10. Aslam, S., Alam, F., Pajooh, H. H., Rashid, M. A., & Asif, H. M. (2021). Machine Learning Applications for Heterogeneous Networks. In Real-Time Intelligence for Heterogeneous Networks: Applications, Challenges, and Scenarios in IoT HetNets (pp. 1-17). Cham: Springer International Publishing.
  11. Aslam, S., Alam, F., Hasan, S. F., & Rashid, M. (2020). A novel weighted clustering algorithm supported by a distributed architecture for D2D enabled content-centric networks. Sensors, 20(19), 5509.
  12. Tito, Shafiqur Rahman, Snjezana Soltic, Barkha Parkash, Attique Ur Rehman, Pieter Nieuwoudt, Saad Aslam, Tek Tjing Lie, Neel Pandey, and M. Daud Ahmed. "Is Edge Computing the Answer for Smart Building Energy Management System?." In 2021 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP), pp. 378-383. IEEE, 2021.
  13. Aslam, S., Alam, F., Hasan, S. F., & Rashid, M. (2019, November). Decentralized interference mitigation technique for D2D networks using game theory optimization. In 2019 29th International Telecommunication Networks and Applications Conference (ITNAC) (pp. 1-7). IEEE. 
  14. Aslam, S., Alam, F., Hasan, S. F., & Rashid, M. (2019, November). Performance analysis of clustering algorithms for content-sharing based D2D Enabled 5G networks. In 2019 29th International Telecommunication Networks and Applications Conference (ITNAC) (pp. 1-7). IEEE.
     

Achievements & Accolades

  1. Research Excellence Award (nominated by school)
  2. Research Excellence Award (2021)
  3. Teaching Excellence Award (2020)
  4. Highly Commended Research Paper – ITNAC (2019)
  5. National Examiner (2019 – 2022) – New Zealand Board for Engineering Diplomas
  6. Massey University Doctoral Scholarship (2017)
  7. Lecturer of the year (2017)
  8. Best Teacher Award (2017)
  9. Honorarium (2016) 
  10. Highest Grade Point Average (CGPA) – MS Electrical Engineering (2014).
  11. Merit Based Undergraduate Scholarship (2006 - 2010)
     

Professional Associations

IEEE