Bio

Hoda S. Hashemi is a postdoctoral scholar at the Ultrasound Imaging & Instrumentation Lab at Stanford University. She received her PhD in Electrical and Computer Engineering from the University of British Columbia (UBC) in 2023. She was also an ultrasound research intern in research and innovation team at DarkVision Technologies Inc. from 2021 to 2023. She holds a M.A.Sc. from Concordia University and a B.Sc. from Sharif University of Technology. Her research interests are ultrasound molecular imaging, elastography and AI in medical image processing. Her research has been funded by the NIH T32 Fellowship at Stanford, the Canadian NSERC Postdoctoral Fellowship, and the Ultrasound Imaging & Instrumentation Lab at Stanford University.

Honors & Awards

  • NIH T32 (SCIT) Fellowship, Stanford University (2024-2025)
  • NSERC Postdoctoral Fellowship, Natural Sciences and Engineering Research Council of Canada (2024-2025)
  • President’s Academic Excellence Award, The University of British Columbia (2020-2023)
  • Faculty of Applied Science Graduate Award, The University of British Columbia (2017-2023)
  • Four Year Fellowship (4YF) Award, The University of British Columbia (2017-2021)
  • ECE MASc. Convocation Award (Best MSc. student), Concordia University (2018)
  • Power Corporation Of Canada Scholarship, Concordia University (2016-2017)
  • The Clara Strozyk Scholarship, Concordia University (2015-2016)

Professional Education

  • Doctor of Philosophy, University of British Columbia, Electrical and Computer Engineering (2023)
  • Master of Applied Science, Concordia University, Electrical and Computer Engineering (2017)
  • Bachelor of Science, Sharif University of Technology, Electrical and Computer Engineering (2014)

Stanford Advisors

Additional Info

All Publications

  • UltraFlex: Iterative Model-Based Ultrasonic Flexible-Array Shape Calibration IEEE TRANSACTIONS ON ULTRASONICS FERROELECTRICS AND FREQUENCY CONTROL Frey, B. N., Hyun, D., Simson, W., Zhuang, L., Hashemi, H. S., Schneider, M., Dahl, J. J. 2025; 72 (11): 1462-1475

    Abstract

    UltraFlex is an iterative model-based ultrasonic flexible-array shape calibration framework that uses automatic differentiation. This work evaluates array shape calibration model performance while examining multiple image quality metrics: speckle brightness, envelope entropy, coherence factor, lag-one coherence, common-midpoint correlation coefficient (CMCC), and common-midpoint phase error (CMPE). The accuracy of these image quality metrics was evaluated on simulated phantoms using a variety of array shapes. Experimental phantom and in vivo liver datasets were also investigated using transducers with known geometries. While speckle brightness, envelope entropy, and coherence factor enable model convergence under many conditions, lag-one coherence, CMCC, and CMPE enable more accurate element position estimations and improved visual ultrasound image focusing quality. Furthermore, the models based on the CMCC and phase-error quality metrics are the most robust against additive white noise while achieving median mean Euclidean errors (MEEs) of 3.7 μm for simulation, 29.7 μm for phantom, and 69.0 μm for in vivo liver data. These array shape calibration results show promise for future development of experimental flexible- and wearableultrasonic arrays.

    View details for DOI 10.1109/TUFFC.2025.3627525

    View details for Web of Science ID 001629793500002

    View details for PubMedID 41171672

  • Enhancing Ultrasound Molecular Imaging: RPCA-Based Filtering to Differentiate Tumor-Bound and Free Microbubbles Hashemi, H. S., Hyun, D., Baek, J., Natarajan, A., Tabesh, F., Paulmurugan, R., Dahl, J. J., IEEE IEEE. 2024

    View details for DOI 10.1109/UFFC-JS60046.2024.10793633

    View details for Web of Science ID 001428150100142

https://orcid.org/0000-0003-3999-2257