Dr. Ashfaq Ahmed
Dr. ashfaq ahmed Postdoctoral Fellow Electrical Engineering

Contact Information
ashfaq.ahmed@ku.ac.ae +97123123219


Dr. Ashfaq Ahmed earned his M.S. and Ph.D. degrees from the Department of Electronics and Telecommunications at Politecnico di Torino, Italy, in 2010 and 2014, respectively. He is presently associated with the Center for Cyber-Physical Systems within the Department of Electrical Engineering and Computer Science at Khalifa University (KU) in Abu Dhabi, UAE. From March 2014 to March 2021, he served as an Assistant Professor at the Department of Electrical & Computer Engineering at COMSATS University Islamabad, Wah campus, Pakistan.

His research interests span hardware security, computational intelligence, evolutionary algorithms, convex optimization, and applied optimization for 5G and beyond 5G applications, among others. He is adept at simulating and modeling optimization problems, utilizing various optimization toolboxes like MATLAB's optimization toolbox and the OPTI toolbox. He has formulated heuristics and applied meta-heuristics to diverse optimization challenges. Recently, his focus has shifted to hardware security and reliable data transmission over wireless channels.

  • PhD (2014), Electronics Engineering, Politecnico di Torino, Italy
  • MS (2010), Electronics Engineering, Politecnico di Torino, Italy
  • BS (2007), Computer Engineering, Bahria University, Islamabad, Pakistan

Affiliated Centers, Groups & Labs

Research Interests
  • Applied optimization for wireless communication applications
  • Hardware security
  • Evolutionary algorithms
  • Artificial intelligence

Research Projects

  • Developed an SPDM chip ensuring zero-trust security; it authenticates guest chips upon connection to our host system, permitting only verified communications.
  • Performed formal verification of the SPDM protocol for thorough validation.
  • Developed a protocol for unmanned traffic management in real-time using remote identification

  • Designed asynchronous NOMA protocols with a novel offline packet repair and recovery system for reliable data delivery
  • Wireless backhauling for IoT applications based on intelligent NOMA without end-device feedback was developed.

Remote Identification based Unmanned Traffic Management System:

An advanced system, combining UAV remote identification (RID) and mobile crowd-sensing (MCS), as traditional methods are unsuitable for large-scale use and deployment. This system quickly identifies UAVs using RID data and integrates ground observer reports for improved monitoring. Advanced machine learning provides real-time RID and image analysis, adapting to different airspace conditions and crowd densities.