Mohammad Ali Salahuddin
Scholar

Mohammad Ali Salahuddin

Google Scholar ID: K6fYz4MAAAAJ
University of Waterloo
Network ManagementNetwork SoftwarizationMachine LearningVANETsCDNs
Citations & Impact
All-time
Citations
3,841
 
H-index
27
 
i10-index
39
 
Publications
20
 
Co-authors
20
list available
Resume (English only)
Academic Achievements
  • His co-authored research publications have received numerous awards, including the Kenneth C. Sevcik Outstanding Student Paper Award at ACM SIGMETRICS, multiple IEEE/IFIP NOMS Best Paper Awards, IEEE CNOM Best Paper, and recognitions such as IEEE Xplore Innovation Spotlight. He serves on the Technical Program Committee (TPC) for international conferences and is a reviewer for various peer-reviewed journals, magazines, and conferences. He is also the vice-chair of the Communications Society in IEEE Kitchener-Waterloo Section.
Research Experience
  • Prior to joining the University of Waterloo, he was a postdoctoral research associate at the Université du Québec à Montréal and a Visiting Scientist at the Concordia Institute for Information Systems Engineering. He also worked in software engineering for the printing and publishing industry with companies like Flint Group and Quark Software.
Education
  • Ph.D. in Computer Science from Western Michigan University, 2014, Advisors: Ala Al-Fuqha (Chair), Dionysios Kountanis, and Mohsen Guizani; M.S. in Computer Science from Western Michigan University, 2003; M.S. in Computer Science from Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology, 2001; B.S. in Computer Science from the University of Karachi, 1999.
Background
  • Research interests include wireless sensor networks, indoor and outdoor localization techniques, QoS and QoE in vehicular ad hoc networks, software-defined networking, network function virtualization, content delivery networks, internet of things, network security, and autonomous network management. He has extensive experience in operations research, machine learning, evolutionary computing, and high-performance computing using CUDA-enabled GPUs.