Biomedical Engineering Ph.D. student researching at the intersection of neuroscience, electrophysiology, and machine learning to develop personalized neuromodulation therapies for chronic pain. My work focuses on identifying neurophysiological biomarkers using EEG, EMG, ECG, wearable sensors, and signal processing to enable quantitative pain assessment, phenotype-driven neuromodulation for neurodegenerative diseases, and innovative neurotechnologies such as real-time brain and spinal mapping, smart wearable integration, and biomarker-guided stimulation. Passionate about translating cutting-edge research into clinical solutions to improve patient outcomes and pioneer next-generation therapeutics.

As a complement, I focus on developing efficient AI-based models, including ViTs and LLMs, for early detection of neurodegenerative and oncological diseases, designing lightweight, real-time diagnostic tools optimized for clinical deployment.

Ph.D. in Biomedical Engineering (BME)
The University of Arizona, Tucson, AZ, USA | 2024 – Present
Advisor: Dr. Ilknur Telkes

M.Sc. in Biomedical Engineering (BME)
Seraj University, Tabriz, East Azerbaijan, Iran | 2020 – 2022
Thesis: Multimodal Data Fusion for Medical Image Analysis Using Machine Learning
Advisor: Dr. Saman Rajebi
GPA: 17.98 / 20 (≈ 3.54 / 4.0)

B.Sc. in Biomedical Engineering (BME)
Islamic Azad University, Tabriz, East Azerbaijan, Iran | 2010 – 2014
GPA: 16.27 / 20 (≈ 3.32 / 4.0)

Supervisor, Clinical Engineer (SCE)
Tabriz University of Medical Sciences, East Azerbaijan, Iran | 2014 – 2024
Industry: Hospitals & Health Care
Office: CED 311, V.C. Treatment Affairs Building, Tabriz University of Medical Sciences, University Main Street, Tabriz, East Azerbaijan, Iran.
Responsibility: Overseeing and leading biomedical engineering teams in hospitals to ensure safe and effective medical devices and healthcare systems for optimal patient care.

GRA: [Telkes Lab] Biomedical Engineering Department, University of Arizona – Dr. ilknur Telkes | 2025 – Present
Executive: [ICERP21-22] Iran COVID-19 Emergency Response Project, WHO – Dr. Jaffar Hussain | 2020 – 2021
UGTA: [BME090] Introduction to Clinical Engineering, Tabriz University – Dr. Danishvar | 2016 – 2017
UGTA: [BME020] Equipment of Hospitals & Medical Centers, Islamic Azad University of Tabriz – Dr. Hashemiaghdam | 2013 – 2014
UGTA: [BME006-8] Computer Programming & Algorithm Calculus, Islamic Azad University of Tabriz – Dr. Rajabioun | 2012 – 2013

[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

Peer Reviewer: IEEE Access: The Multidisciplinary Open Access Journal | January 2025 – Present 🔗 Journal | 🔗 Web of Science

[Herbold Fellowship]: Awarded by the College of Engineering, University of Arizona ($58,470) | 2024 - 2025
[Excellence Award, CE]: Recognized for outstanding performance in hospital-related affairs, TUoMS | 2015 – 2024
[Appreciation Award]: Honoured for contributions as Executive Assistant during the ICERP, WHO | 2020 – 2021
[Student Recognition Award]: For scientific and executive contributions to the BMES Association, IAUT | 2011 – 2014

• Azerbaijani (Azeri) – Native / Mother Tongue
• Persian (Farsi) – Native / National Language
• Turkish – Professional Proficiency
• English – Professional Proficiency

Journal 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., Kozak, I., & Lee, E.J. (2025). ViT-2SPN: Vision Transformer-Based Dual-Stream Self-Supervised Pretraining for Retinal OCT Classification. arXiv preprint arXiv:2501.17260. 🔗 Preprint | 📄 Slide
• 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

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