I am a biomedical engineering Ph.D. student at the University of Arizona specializing in scientific machine learning, biomarker discovery, and biomedical data analysis. My primary focus lies in translating multimodal data into actionable clinical insights.

Core Expertise and Achievements:

• As an intern at Roche, I've developed an automated whole-slide image analysis tool using machine learning, tissue segmentation, and stain deconvolution to quantify cellular structures and assess stain quality in HTX-DAB–stained tissues, supporting R&D and QC operations.

• As a graduate research assistant, I've analyzed EEG signals in spinal cord stimulation for chronic pain biomarker discovery in collaboration with Medtronic and NINDS.

• As a supervisor clinical engineer with nearly a decade of experience, I've led clinical engineering teams in medical device management and collaborated with the World Health Organization to optimize data-driven patient care and healthcare operations during the COVID-19 pandemic.

Complementing my technical expertise, I possess professional working proficiency in English, Turkish, Azerbaijani, and Persian, enabling seamless collaboration within global, cross-functional teams.

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

Summer Intern: Pathology Lab Research & Early Development, Roche (Ventana Medical System), Tucson, USA | Summer 2026

[1] Project [PL-RED]: QuHTX: Nuclei and Non-Nuclei Quantification in HTX–DAB-Stained Tissue

My role is to develop an automated, end-to-end whole-slide image analysis tool for HTX- and DAB-stained tissues (liver, kidney, colon, skin, and tonsil). The high-throughput pipeline supports R&D and Manufacturing Operations by reducing manual touchpoints, turnaround time, and costs. It integrates tissue segmentation, optical density-based stain deconvolution, nuclei segmentation, non-nuclei estimation, and advanced colorimetric metrics (hue angle, stain uniformity, white balance, and HTX-DAB overlap) to deliver robust quantitative analysis and visualization.

Graduate Research Assistant: Telkes Lab, BME & Neurosurgery Department, University of Arizona, Tucson, USA | 2025 – Present

[1] Project [Co-Op: Medtronic & UZ Brussels]: STARGATE (2026 – Present)

My role is in multimodal data analysis, where I focus on data strategy and analytical execution to characterize physiological signals in chronic pain. I perform integrated analyses of multimodal biosignals, including EEG, ECG, and ECAPs, across varying clinical conditions and body positions. My work focuses on developing robust analytical frameworks that translate complex neural and cardiac signals into actionable clinical insights.

[2] Project [NINDS: R00NS119672]: Post-Op SCS for Discovering Biomarkers of Chronic Pain (2025 – 2026)

My role involved developing end-to-end MATLAB pipelines for neurophysiological signal processing and machine learning to quantify oscillatory pain neuromarkers and cortical dynamics during Long- and Short-Term spinal cord stimulation (SCS). Developed EEG acquisition and analysis frameworks to extract spectral and connectivity metrics, and analyzed motor–sensory and brain–spine interactions to identify connectivity patterns linked to SCS efficacy.

Graduate Research Assistant: VSI Lab, ECE Department, University of Arizona, Tucson, USA | 2024 – 2025

My role involved designing and implementing deep learning pipelines for medical object detection and multimodal sensor fusion, incorporating triple adaptive mechanisms and a self-supervised pretraining framework that leverages contrastive learning, adaptive layer weighting, and token-level attention initialized in Python (TensorFlow & Keras) workflows.

Supervisor, Clinical Engineer: Tabriz University of Medical Sciences, East Azerbaijan, Tabriz, Iran | 2014 – 2024

My role involved implementing machine learning frameworks incorporating feature extraction and ensemble methods for multimodal clinical data analysis, while leading clinical engineering teams in overseeing capital and disposable medical device management, operational training, preventive maintenance, statistical analysis, clinical readiness, and collaboration with the WHO on COVID-19 response to ensure safe, efficient, and effective healthcare operations.

Department Assistant: Dean's Office, BME Department, University of Arizona, Tucson, USA | Summer 2025

My role involved managing alumni records, supporting student orientation events, and guiding program resources, research opportunities, and departmental activities.

Project Assistant: Iran COVID-19 Emergency Response Project, World Health Organization | 2020 – 2021

My role involved 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

Peer-Reviewer:

• IEEE Access, New York, NY, USA | 2025 – Present
• CaPTion @ MICCAI, Strasboug, France | 2026
• MIUA, Dublin, Ireland | 2026

[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

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