Amir Shamaei - Biomedical Engineer

Professional Summary

Biomedical engineer with a Ph.D. and expertise in developing artificial intelligence and software solutions for magnetic resonance spectroscopy and imaging. Seeking to leverage my strong research background, programming skills, and experience developing software to advance the field of biomedical imaging. Passionate about applying machine learning techniques to improve the analysis and interpretation of complex medical imaging data, ultimately enhancing patient care.

Education

Ph.D. in Biomedical Engineering, Brno University of Technology (2019 - 2023)

Dissertation: Development of new artificial intelligence functionalities for magnetic resonance spectroscopic signals processing
Supervisor: Dr. Radovan Jiˇr´ık
Co-Supervisors: Dr. Zenon Starˇcuk, Dr. Jana Starˇcukov´a

M.Sc. in Biomedical Engineering, Amirkabir University of Technology (2015 - 2018)

Dissertation: Design and fabrication of a Lab-on-chip device for assessment of cancer cells viability during electrical stimulation
Supervisor: Dr. Saviz
Co-Supervisors: Dr. Solouk, Dr. Abdullahad

B.Sc. in Electrical Engineering, Zanjan University (2010 - 2015)

Dissertation: Wireless System Network Simulator Software
Supervisor: Dr. Babazadeh

Experience

Teaching Experience, University of Calgary and the Southern Alberta Institute of Technology (SAIT), Canada (2023-Present)

Advance Image processing and machine learning (co-instructor)
Operating systems
Software security

Postdoctoral Fellow, Advanced Imaging and Artificial Intelligence Lab, University of Calgary, Canada (2023-Present)

Developing AutoDL reconstruction methods
Incorporation of redundancies in the data, such as past subject-specific information via deep learning
Developing a software application called MRIntelligence to integrate MRI

Marie Curie Fellow, INSPiRE-MED consortium (2019 - 2023)

Lead research applying deep learning for medical image analysis and reconstruction
Published 6 papers in top conferences and journals
Designed open-source solutions for medical image processing using Python and Java
Ensured compliance with regulatory standards and quality assurance protocols, contributing to the CE/FDA approval of the developed software

Peer-Reviewed Publications

Shamaei, A., Saviz, M., Solouk, A. et al. (2020). An In Vitro Electric Field Exposure Device with Real-Time Cell Impedance Sensing. Iran J Sci Technol Trans Sci 44, 575–585. doi.org/10.1007/s40995-020-00861-z, Impact factor: 2.4
Shamaei, A., Starcukova, J., Pavlova, I., Starcuk, Z. (2023). Model-informed unsupervised deep learning approaches to frequency and phase correction of MRS signals. Magn Reson Med. 89: 1221– 1236. doi:10.1002/mrm.29498, Impact factor: 3.3
Clarke, W., Mikkelsen, M., Oeltzschner, G., Bell T.K., Shamaei, A., Soher, B.J., Emir, U., Wilson, W. (2022). A standard data format for magnetic resonance spectroscopy. Magnetic Resonance in Medicine 1- 13. doi:10.1002/mrm.29418, Impact factor: 3.3
Rizzo, R., Dziadosz, M., Kyathanahally, S.P., Shamaei, A., Kreis, R. (2022). Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias. Magn Reson Med. 1- 21. doi:10.1002/mrm.29561, Impact factor: 3.3
Shamaei, A., Starcukova, J., Starcuk, Z. (2023). Physics-informed Deep Learning Approach to Quantification of Human Brain Metabolites from Magnetic Resonance Spectroscopy Data. Computers in Biology and Medicine. 158: 106837. doi:10.1016/j.compbiomed.2023.106837, Impact factor: 7.7
Shamaei, A., Starcukova J., Rizzo R., Starcuk Z. (2024). Water removal in MR spectroscopic imaging with Casorati singular value decomposition. Magn Reson Med. 91(4): 1694-1706. doi: 10.1002/mrm.29959, Impact factor: 3.3

Conference Presentations

Shamaei, A., Starˇcukov´a J., Starcuk Jr. Z. (2023). EigenMRS: A Computationally Cheap Data-Driven Approach to MR Spectroscopic Imaging Denoising. Poster presentation delivered in person at ISMRM 2023, June, 2023.
Shamaei, A., Starˇcukov´a J., Jedrek Burakiewicz, Starcuk Jr. Z. (2023). Water Removal In MR Spectroscopic Imaging with Casorati Singular Value Decomposition. Poster presentation delivered in person at ISMRM 2023, June, 2023.
Shamaei, A., Rizzo, R. (2023). Physics-Informed Deep Learning Approach to Quantifying Magnetic Resonance Spectroscopy Data with Simultaneous Uncertainty Estimation. Power pitch presentation delivered in person at ISMRM 2023, June, 2023.
Shamaei, A., Starˇcukov´a J., Radim Koˇr´ınek, Starcuk Jr. Z. (2022). Magnetic Resonance Spectroscopic Imaging Data Denoising by Manifold Learning: An Unsupervised Deep Learning Approach. Poster presentation delivered in person at ISMRMB 2022, May, 2022.
Shamaei, A., Starˇcukov´a J., Starcuk Jr. Z. (2021). Frequency and Phase Shift Correction of MR Spectra Using Deep Learning in Time Domain. Poster presentation delivered virtually at ESMRMB 2021, October, 2021.
Clarke, W., Bell, T., Emir, U., Mikkelsen, M., Oeltzschner, G., Rowland, B., Shamaei, A., Soher, B., Tapper, S., Wilson, M. (2021). NIfTI MRS: A standard format for spectroscopic data. Poster presentation delivered in person at ISMRMB 2021, May, 2021.
Shamaei, A., Starˇcukov´a J., Starcuk Jr. Z. (2021). A Wavelet Scattering Convolution Network for Magnetic Resonance Spectroscopy Signal Quantitation. Oral Presentation delivered virtually at the 14th International Joint Conference on Biomedical Engineering Systems and Technologies, Vienna, February, 2021. 10.5220/0010318502680275.
Shamaei, A., Jiˇr´ık R., Starˇcukov´a J. (2020). Deep Learning For Magnetic Resonance Spectroscopy Quantification: A Time-Frequency Analysis Approach. Presented in The 26th Conference Student EEICT, Brno University of Technology, March, 2020. WOS:000598376500032.
Shamaei, A., Saviz, M. (2017). Voltage Transfer Functions for In-Vitro Cell Stimulation: A Computational Study. Poster presentation delivered at IEEE Conference on Biomedical Engineering, Amirkabir University of Technology, October, 2017. 10.1109/ICBME.2017.8430245.