Ph.D. student in Biomedical Engineering with expertise in neuroengineering and multimodal physiological signal analysis, including EEG, ECG, EMG, wearable biosensors, and fNIRS. Skilled in biosignal analytics, wearable sensing, and machine learning (ML), with hands-on experience integrating algorithms into experimental and real-time neural recording systems. Current Research rigorously investigates the brain–spine neural axis, examining frequency dynamics, pulse patterns, electrode–tissue interactions, sensory-motor control, and neural connectivity to advance personalized neuromodulation therapies. Proficient in MATLAB and Python for developing signal processing pipelines, ML algorithms in experimental research, and aiming to leverage this expertise in EEG-based BCI, personalized neuromodulation, interactive neural signal processing, and ML to enhance non-invasive systems in applied research.
Ph.D., Biomedical Engineering
The University of Arizona, Tucson, AZ, USA | 2024 – Present
M.Sc., Biomedical Engineering
Seraj University, Tabriz, East Azerbaijan, Iran | 2020 – 2022
B.Sc., Biomedical Engineering
Islamic Azad University, Tabriz, East Azerbaijan, Iran | 2010 – 2014
Graduate Research Assistant: Telkes Lab, BME & Neurosurgery Department, University of Arizona, Tucson, USA | 2025 – Present
- Led EEG signal processing and ML pipelines in Python and MATLAB to quantify oscillatory biomarkers of chronic pain and waveform-specific cortical dynamics during spinal cord stimulation (SCS).
- Analyzed multimodal physiological data (EEG, ECG, EMG, ActiGraph, fNIRS) and conducted high-density EEG recordings, developing custom MATLAB frameworks to extract spectral, coherence, and functional connectivity metrics.
- Investigated motor–sensory interactions and brain–spine dynamics, performing group-level analyses to identify connectivity patterns linked to SCS efficacy.
Graduate Research Assistant: VSI Lab, ECE Department, University of Arizona, Tucson, USA | 2024 – 2025
- Designed and implemented deep learning models (CNNs, ViTs) with triple adaptive mechanisms for medical object detection and multimodal sensor-fusion pipelines.
- Developed a self-supervised pretraining framework using contrastive learning, adaptive layer weighting, and token-level attention; surveyed and integrated SOTA architectures with hybrid attention, CSP networks, Spatial Pyramid Pooling, and BiFPN in efficient Python/TensorFlow/Keras pipelines.
Supervisor, Clinical Engineer: Tabriz University of Medical Sciences, East Azerbaijan, Tabriz, Iran | 2014 – 2024
- Developed ML frameworks (AutoCML, AutoIFSCML) with feature engineering and ensemble methods for multimodal clinical data analysis.
- Led clinical engineering teams and collaborated with WHO on COVID-19 response, managing medical devices, training, maintenance, data analysis, and clinical readiness to ensure safe and efficient operations.
Journal Peer-Reviewer: IEEE Access, New York, NY, USA | 2025 – Present
- Reviewed AI/ML manuscripts for methodological rigor, model design, and reproducibility, providing constructive feedback to improve clarity and overall impact.
Department Assistant: Dean's Office, BME Department, University of Arizona, Tucson, USA | Summer 2025
- Managed alumni records, supported student orientation events, and guided program resources, research opportunities, and departmental activities.
Project Assistant: Iran COVID-19 Emergency Response Project, World Health Organization | 2020 – 2021
- Supported the WHO ICERP by managing medical equipment across hospitals and analyzing electronic health records to inform data-driven decisions and optimize patient care during the pandemic.
Undergraduate Teaching Assistant: East Azerbaijan, Tabriz, Iran
- [BME 090] Introduction to Clinical Engineering, Tabriz University – Dr. Sebelan Danishvar | 2016 – 2017
- [BME 020] Equipment of Hospitals & Medical Centers, Islamic Azad University of Tabriz – Dr. Hashemiaghdam | 2013 – 2014
- [BME 006-8] Computer Programming & Algorithm Calculus, Islamic Azad University of Tabriz – Dr. Rajabioun | 2012 – 2013
| EEG/Bio Signals (Hands-On) | Programming & ML |
|---|---|
| EEG, ECG, EMG, fNIRS, Wearable Biosensors | MATLAB (EEGLAB, Simulink, FieldTrip) |
| Neural Signal Processing Pipelines | Python (NumPy, Pandas, Matplotlib, Scikit-learn) |
| Spectral/Time/Frequency Analysis, Connectivity, ERP | AI/ML: Classical ML, CNN, ViT, TensorFlow, Keras |
| Neuroengineering | Tools & Documentation |
|---|---|
| Brain–Spine Dynamics, Motor-Sensory Interactions | LaTeX/Overleaf, Statistics, MS Office, EndNote, SQL |
| Experimental Design, Electrode–Tissue Interaction | Jupyter, VS Code, Anaconda (Spyder) |
| Languages Proficiency | Personal & Social |
|---|---|
| English: C1, Turkish: B2, Azerbaijani: C2, Persian: C2 | Organization, Time Management, Communication |
[On-Site] VSI, ECE, University of Arizona, AZ, USA – Dr. Eungjoo Lee | 2024 - 2025 🔗 Paper
[Remote] CHI, AIHI, Macquarie University, NSW, Australia – Dr. Sidong Liu | 2023 – 2024 🔗 Paper
[Remote] CEDP, Brunel University London, London, UK – Dr. Sebelan Danishvar | 2022 – 2023 🔗 Paper
- Saraei, M., Lee, E.J., & Lalinia, M. (2025). Deep Learning-Based Medical Object Detection: A Survey. IEEE Access (EMBS), 13, 53019–53038. 🔗 DOI | PDF
- Saraei, M., & Liu, S. (2023). Attention-Based Deep Learning Approaches in Brain Tumor Image Analysis: A Mini Review. Frontiers in Health Informatics, 12, 164. 🔗 DOI | PDF
- Saraei, M., Rahmani, S., Rajebi, S., & Danishvar, S. (2023). A Different Traditional Approach for Automatic Comparative Machine Learning in Multimodality COVID-19 Severity Recognition. Int. J. Innov. Eng., 3(1), 1–12. 🔗 DOI | PDF
[Research Assistantship]: Supported by the Telkes Lab, University of Arizona ($71,158) | 2025 - Present
[Herbold Fellowship]: Awarded by the College of Engineering, University of Arizona ($58,470) | 2024 - 2025
© 2025 Mohammadreza Saraei · All rights reserved!
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