Curriculum Vitae
Profile
Machine learning and computer vision engineer with more than five years of experience across biomedical signal processing, biometric recognition, real-time video analytics, and research software.
My work covers data preparation, preprocessing, model development, evaluation, experiment design, performance optimization, and the development of modular research pipelines.
Research and Engineering Experience
Computer Vision Research Collaborator — Vrije Universiteit Brussel
2026–Present
Current research collaboration on ocular computer vision from RGB video, including eye-region analysis, pupil localization and size estimation, temporal tracking, gaze-related measurements, and evaluation under varying illumination, viewing conditions, head movement, and image quality.
Research Fellow — University of Milan
March 2024–February 2025
Developed modular Python research software for biometric machine learning, including an iris-recognition framework integrating more than 30 classical and deep-learning methods behind reusable interfaces. The work covered systematic experimentation and reproducible evaluation for ocular images captured using conventional cameras under uncontrolled conditions, alongside research in ECG biometrics and physiological-signal processing.
AI/ML Engineer (Consultant) — Road Traffic Injury Research Center
January 2020–January 2023
Applied computer vision for roadside-video analysis, including vehicle detection, tracking, speed estimation, inter-vehicle distance measurement, data annotation, model validation, and robustness analysis under uncontrolled environmental conditions.
Research Assistant — University of Tabriz
February 2019–February 2024
Research in motor-imagery EEG, brain-computer interfaces, signal processing, machine learning, deep learning, and comparative experimental evaluation.
Selected Research Projects
ECG-biometrics-bench
Development of a research framework for reproducible ECG-biometric experimentation and evaluation, with emphasis on standardized workflows, dataset handling, preprocessing, biometric evaluation protocols, testing, and reproducibility.
The associated solo-authored paper is available as a 2026 arXiv preprint and is currently under review.
Iris Recognition Framework
Developed during my research at the University of Milan, this modular Python framework integrated more than 30 classical and deep-learning iris-recognition methods behind reusable interfaces, supporting systematic comparison and reproducible experimentation across multiple stages of the biometric pipeline.
Education
M.Sc. in Electrical Engineering — Control Engineering University of Tabriz, 2016–2019
Thesis: Classification of Motor Imagery EEG Signals Using Deep Learning Methods
B.Sc. in Electrical Engineering Amirkabir University of Technology (Tehran Polytechnic), 2011–2016
Technical Skills
Programming: Python, C++, MATLAB, SQL, Bash
Machine Learning: PyTorch, TensorFlow/Keras, scikit-learn, CNNs, recurrent neural networks, Transformers, representation learning, model evaluation
Computer Vision: OpenCV, image and video processing, object detection, tracking, segmentation, biometric recognition
Biomedical Signals: EEG/BCI, ECG, signal preprocessing, feature extraction, time-frequency analysis
Research and ML Engineering: reproducible experimentation, data pipelines, benchmarking, Docker, FastAPI, REST APIs, pytest, Git, GitHub Actions, GitLab CI/CD, Linux
Teaching and Service
- Teaching Assistant, Optimal Control, University of Tabriz
- Teaching Assistant, Linear Control Systems, University of Tabriz
- Vice President, IEEE Student Branch, University of Tabriz, 2017–2018
- Research mentoring and support for undergraduate student projects
Publications
See the complete Publications page.
For additional information, please contact me.