Mahmoud Ebrahimkhani

About

Dr. Mahmoud Ebrahimkhani earned his Bachelor’s degree in Electrical Engineering and both his Master’s and Ph.D. in Biomedical Engineering from Stony Brook University. His doctoral research focused on developing machine learning and deep learning models for terahertz spectroscopy and spectral imaging to enhance the prediction of histological markers in burn injuries, thereby aiding wound healing treatment planning.
Following his Ph.D., he joined Northwestern University as a postdoctoral research associate, where he applied deep learning techniques to medical imaging. He specialized in utilizing Generative Adversarial Networks (GANs) to estimate three-dimensional aortic hemodynamics from CT angiography data.
Currently, Dr. Ebrahimkhani is a Machine Learning Scientist at a biotech startup. He designs, trains, and deploys deep learning models for chemoinformatics and bioinformatics applications, including de novo small molecule design, ADMET property prediction, protein engineering, and protein–ligand docking. His expertise encompasses generative AI models like diffusion models and architectures such as transformers, graph neural networks, recurrent neural networks, and convolutional neural networks.
Dr. Ebrahimkhani has authored 40 peer-reviewed journal and conference articles and holds patents. His work has accumulated 362 citations.

Selected Publications

• Collins et al., “Attentive graph neural network models for the prediction of blood brain barrier permeability,” Nat. Communications submitted.
• Ebrahimkhani et al., “A deep learning approach to using wearable seismocardiography (SCG) for diagnosing aortic valve stenosis and predicting aortic hemodynamics obtained by 4D Flow MRI,” Ann. Biomed. Eng. 2023.
• Ebrahimkhani et al., “Triage of in vivo burn and prediction of wound healing outcome using neural networks and modeling of the terahertz permittivity based on the double Debye dielectric parameters,” Biomed. Opt. Express 2023.
• Ebrahimkhani et al., “Deep learning-based prediction of aortic hemodynamics obtained by 4D flow MRI using seismocardiography of chest vibrations,” ISMRM 2023.
• Berhane et al., “Highly resilient AI-derived 3D aortic hemodynamics from aortic geometry,” ISMRM 2023.
• Ebrahimkhani et al., “Prediction of aortic hemodynamics using convolutional neural networks (CNN) and time-frequency transformation of chest vibrations measured by seismocardiography (SCG),” SCMR 2022.
• Ebrahimkhani et al., “Physical modeling of the permittivity of in vivo burn injuries using Debye dielectric parameters measured by the THz PHASR scanner,” Proc. Conf. Lasers Electro-Opt. 2022.
• Ebrahimkhani et al., “Supervised machine learning for automatic classification of in vivo scald and contact burn injuries using the terahertz Portable Handheld Spectral Reflection (PHASR) Scanner,” Sci. Rep. 2022.
• Ebrahimkhani et al., “Accurate and early prediction of the wound healing outcome of burn injuries using the wavelet Shannon entropy of THz time-domain waveforms,” J. Biomed. Opt. 2022.
• Ebrahimkhani et al., “Multiresolution spectrally-encoded terahertz reflection imaging through a highly diffusive cloak,” Opt. Express 2022.
• Ebrahimkhani et al., “Translation-invariant zero-phase wavelet methods for feature extraction in terahertz time-domain spectroscopy,” Sensors 2022.
• Osman et al., “Deep neural network classification of in vivo burn injuries with different etiologies using terahertz time-domain spectral imaging,” Biomed. Opt. Express 2022.
• Ebrahimkhani et al., “Diffuse terahertz spectroscopy in turbid media through a wavelet-based bimodality spectral analysis,” Sci. Rep. 2021.
• Ebrahimkhani et al., “Chemical identification in the specular and off-specular rough-surface scattered terahertz spectra using wavelet shrinkage,” IEEE Access 2021.
• Ebrahimkhani et al., “Acute burn assessment using terahertz spectroscopic feature extraction and support vector machines,” Proc. Conf. Lasers Electro-Opt. 2021.
• Ebrahimkhani et al., “Accurate classification of burn injuries using support vector machines and the wavelet Shannon entropy of the THz-TDS waveforms,” Proc. of IEEE IRMMW-THz 2021.
• Ebrahimkhani et al., “Phase function effects on identification of Terahertz spectral signatures using the discrete wavelet transform,” IEEE Trans. Terahertz Sci. and Technol. 2020.
• Harris et al., “Terahertz time-domain spectral imaging using telecentric beam steering and an f-θ scanning lens: distortion compensation and determination of resolution limits,” Opt. Express 2020.
• Harris et al., “Terahertz portable handheld spectral reflection (PHASR) scanner,” IEEE Access 2020.