Bio

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: https://www.youtube.com/watch?v=axlBCf56_AM, 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

Patents

  • 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

Contact

  • Academic
    shmirjah@stanford.edu
    University - Scholar Department: Radiation Oncology Position: Postdoctoral Scholar

Additional Info

Links

All Publications

  • Computerized Tomography With Low-Frequency Electromagnetic Radiation International Microwave and Antenna Symposium (IMAS) Mirjahanmardi, S., Ba raean, S. M., Akbari-Chelaresi, H., Nayyeri, V., Ramahi, O. 2023
  • Computerized Tomography with Radon Transform using Microwaves and Electrostatics IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting Ramahi, O., Ba Raean, S., Akbari-Chelaresi, H., Mirjahanmardi, S., Nayyeri, V. 2023
  • 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

  • Deep Feature Learning for Microwave Mammography With Convolutional Autoencoders IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting Mirjahanmardi, S., Akbari Chelaresi, H., Sali, R., Ramahi, O. 2023
  • Toward Computerized Tomography With Microwaves IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES Mirjahanmardi, S., Ramahi, O. M. 2022

    View details for DOI 10.1109/TMTT.2022.3205635

    View details for Web of Science ID 000857327400001

  • Ki67 proliferation index quantification using silver standard masks SPIE Medical Imaging Mirjahanmardi, S., Dawe, M., Fyles, A., Shi, W., Androutsos, D., Liu, F., Done, S., Khademi, A. 2022
  • Preserving Dense Features for Ki67 Nuclei Detection Mirjahanmardi, S., Dawe, M., Fyles, A., Shi, W., Liu, F., Done, S., Khademi, A., Tomaszewski, J. E., Ward, A. D., Levenson, R. M. SPIE-INT SOC OPTICAL ENGINEERING. 2022

    View details for DOI 10.1117/12.2611212

    View details for Web of Science ID 000838055900029

  • Computerized Tomography with Radon Transform using Microwaves and Electrostatics IEEE International RF and Microwave Conference (RFM) Ramahi, O., Ba Raean, S., Akbari-Chelaresi, H., Mirjahanmardi, S., Nayyeri, V. 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

    View details for DOI 10.1109/TIM.2021.3062676

    View details for Web of Science ID 000731626300013

  • 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

    View details for DOI 10.1109/TIM.2020.2988329

    View details for Web of Science ID 000571849100092

  • 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)

    Abstract

    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

  • Highly Accurate Liquid Permittivity Measurement using Coaxial Lines Mirjahanmardi, S., Ramahi, O., IEEE IEEE. 2019: 101-102

    View details for Web of Science ID 000657207100049

  • 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

    View details for Web of Science ID 000414980000011

  • 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

    View details for Web of Science ID 000371401400246

https://orcid.org/0000-0001-6063-7028