Xingqi Zhang, PhD

Assistant Professor, Faculty of Engineering - Electrical & Computer Engineering Dept


Assistant Professor, Faculty of Engineering - Electrical & Computer Engineering Dept
(780) 492-1448
11-381 Donadeo Innovation Centre For Engineering
9211 116 St
Edmonton AB
T6G 2H5


Area of Study / Keywords

Applied Electromagnetics Electromagnetics and Microwaves Communications Engineering Radio Wave Propagation Antennas & RF/Microwave Design 5G/6G/THz Integrated Circuits and Systems IoT Biomedical Engineering Software Engineering and Intelligent Systems Machine Learning


Dr. Xingqi Zhang received the B.Sc. degree (graduated with honors) from the Harbin Institute of Technology, China, and the M.A.Sc. and Ph.D. degrees from the University of Toronto, Canada. He is currently an Assistant Professor in the Department of Electrical and Computer Engineering, University of Alberta, Canada. He is also affiliated with the School of Electrical & Electronic Engineering, University College Dublin, Ireland, as an Adjunct Professor. Besides, he has been a Visiting Professor at the University of Toronto and the Queen Mary University of London, and an Associate Investigator at the Science Foundation Ireland Research Centre for Future Networks and Communications (CONNECT) in Ireland.

His research is in the interdisciplinary areas of applied electromagnetics and wireless communications. A particular focus is on the development of high-performance computational models/algorithms for emerging wireless technologies in 5G/6G/THz wireless communications, intelligent transportation (air, ground, underground), underwater communications, industrial Internet of Things, as well as biomedical sensing and healthcare applications. 

He has been involved in a variety of research council and industry-funded projects, and has been collaborating with many world-leading universities and international companies. For example, he is currently the coordinator and lead principal investigator (PI) for a million-level European-funded project under the Horizon 2020 Future & Emerging Technologies program. The projects in his group cover the following topics: wireless channel modeling and optimization in indoor, urban, and terrestrial environments, multiphysics and multiscale modeling for electromagnetic, micro-/nano-electronic, and biomedical problems, indoor/outdoor localization, integrated sensing and communication, machine learning and parametric modeling, stochastic uncertainty quantification, EMC/EMI analysis, reconfigurable intelligent surface (RIS), as well as antenna and RF/microwave/millimeter-wave design & measurement.

He has served as an Associate Editor for the IEEE Antennas and Wireless Propagation Letters (AWPL), the IET Microwaves, Antennas & Propagation (MAP), and the IEEE Journal on Multiscale and Multiphysics Computational Techniques (JMMCT), as well as a Technical Program Committee Member and Session Chair for several flagship conferences, including the IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO), the IEEE International Symposium on Antennas and Propagation (AP-S), etc. He was the recipient of the RIA Charlemont Award from the Royal Irish Academy, the IRC Research Ally Prize from the Irish Research Council, the Outstanding Young Scholar Award from the IEEE ICCT, several Young Scientist Awards from the International Union of Radio Science (URSI), the Applied Computational Electromagnetics Society (ACES), and the Electromagnetics Academy, as well as Best Paper Awards at international symposia.


Google Scholar

Website (UCD)


Research Interests:

  • Physics-based wireless channel modeling and optimization of sensor networks in cyber-physical systems. 
  • Radio wave propagation (indoor, urban, terrestrial, body-centric), integrated sensing & communication, localization. 
  • Multiphysics/multiscale modeling for electromagnetic, micro-/nano-electronic, and biomedical applications. 
  • Reconfigurable intelligent surface (RIS), antenna & RF/microwave/millimeter-wave design, EMC/EMI.
  • Machine learning/parametric modeling, physics-informed neural network, stochastic uncertainty quantification.



Dr. Xingqi Zhang is currently looking for highly motivated Master/PhD students, Postdocs, and Visiting Researchers to join his group. For interested candidates, please feel free to drop him an email along with your CV, academic transcripts, English qualifications, and any other documents that you believe can well demonstrate your research capability and potential.