Seyed Hossein Mirjahanmardi currently serves as a postdoctoral fellow in Medical Physics at Stanford University. He earned his Ph.D. with honors in electrical and computer engineering from the University of Waterloo, Canada, in 2020. Dr. Mirjahanmardi is a senior member of IEEE and has been honored with the Natural Sciences and Engineering Research Council of Canada (NSERC) fellowship award. His expertise and industry experience extend from Electromagnetics and RF design to Computational Pathology and High-dimensional Data Analysis, primarily focusing on Artificial Intelligence (AI) algorithms. His focus is inventing technologies in AI, computational pathology, and Electromagnetics for healthcare applications. His PhD research and current works at Stanford have been highlighted widely in the news.

Honors & Awards

  • NSERC PDF Award, Natural Sciences and Engineering Research Council of Canada (2023)
  • Research Seed Grant, School of Medicine, Stanford University (2023)
  • IEEE Senior Member, IEEE (2022)
  • First Place Award at 3MT Presentation, YouTube Link:, University of Waterloo (2018)
  • Ontario PhD Nomination Award, Ontario (2020)
  • Outstanding Teaching Sandford Fleming Award, University of Waterloo (2019)
  • 3MT Finalist, University of Waterloo (2018)
  • Best Teaching Assistant Award, University of Waterloo (2018)
  • Best Thesis Presentation Award, University of Waterloo (2018)
  • Best Student Award, University of Waterloo (2017)
  • Best Student Award, University of Waterloo (2016)
  • Best Student Award, Amirkabir University of Technology (2014)

Professional Education

  • Bachelor of Science, University Of Shiraz (2012)
  • Master of Science, Amirkabir University of Technology (2014)
  • Doctor of Philosophy, University of Waterloo (2020)
  • Ph.D., University of Waterloo, Electrical and Computer Engineering (2020)

Stanford Advisors


  • Seyed Hossein Mirjahanmardi, Yuming Jiang, Ruijiang Li. "United States Patent 63504621 Automated Cell Classification on Histopathology Images without Human Annotations", Leland Stanford Junior University, Jun 8, 2023
  • Seyed Hossein Mirjahanmardi, Omar Ramahi. "United States Patent 62909218 Computerized Tomography with Microwaves", Oct 1, 2019

All Publications

  • Low-Dispersive Permittivity Measurement Based on Transmitted Power Only IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting Mirjahanmardi, S., Ramahi, O. 2023
  • Ki67 Proliferation Index Quantification Using Silver Standard Masks Mirjahanmardi, S., Dawe, M., Fyles, A., Shi, W., Androutsos, D., Liu, F., Done, S., Khademi, A., Tomaszewski, J. E., Ward, A. D. SPIE-INT SOC OPTICAL ENGINEERING. 2023

    View details for DOI 10.1117/12.2654599

    View details for Web of Science ID 001011463700019

  • Toward Computerized Tomography With Microwaves IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES Mirjahanmardi, S., Ramahi, O. M. 2022
  • Permittivity Characterization of Dispersive Materials Using Power Measurements IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT Mosavirik, T., Soleimani, M., Nayyeri, V., Mirjahanmardi, S., Ramahi, O. M. 2021; 70
  • Permittivity Reconstruction of Nondispersive Materials Using Transmitted Power at Microwave Frequencies IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT Mirjahanmardi, S., Albishi, A. M., Ramahi, O. M. 2020; 69 (10): 8270-8278
  • Intelligent Sensing Using Multiple Sensors for Material Characterization SENSORS Albishi, A. M., Mirjahanmardi, S. H., Ali, A. M., Nayyeri, V., Wasly, S. M., Ramahi, O. M. 2019; 19 (21)


    This paper presents a concept of an intelligent sensing technique based on modulating the frequency responses of microwave near-field sensors to characterize material parameters. The concept is based on the assumption that the physical parameters being extracted such as fluid concentration are constant over the range of frequency of the sensor. The modulation of the frequency response is based on the interactions between the material under test and multiple sensors. The concept is based on observing the responses of the sensors over a frequency wideband as vectors of many dimensions. The dimensions are then considered as the features for a neural network. With small datasets, the neural networks can produce highly accurate and generalized models. The concept is demonstrated by designing a microwave sensing system based on a two-port microstrip line exciting three-identical planar resonators. For experimental validation, the sensor is used to detect the concentration of a fluid material composed of two pure fluids. Very high accuracy is achieved.

    View details for DOI 10.3390/s19214766

    View details for Web of Science ID 000498834000163

    View details for PubMedID 31684027

    View details for PubMedCentralID PMC6864703

  • Forward Scattering from a Three Dimensional Layered Media with Rough Interfaces and Buried Object(s) by FDTD APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL Mirjahanmardi, S. H., Dehkhoda, P., Tavakoli, A. 2017; 32 (11): 1020-1028
  • Electromagnetic Scattering from a Buried Sphere in a Two-Layered Rough Ground Mirjahanmardi, S. H., Tavakoli, A., Zamani, H., Dehkhoda, P., IEEE IEEE. 2015: 506-507