Deep Learning & Biomedical Engineering
Reza Alinejad
M.Sc. Biomedical Engineering & Researcher
Masters graduate in Biomedical Engineering with hands-on experience in AI/ML research at Forschungszentrum Jülich. Specializing in multimodal data acquisition, computer vision, signal processing, and developing AI algorithms for medical applications.
Experience
Hands-on experience in AI research, medical technology, and biomedical engineering applications.
Research Assistant - Forschungszentrum Jülich (PGI15 - Neuromorphic Software Ecosystems)
Extending the dataset and the baseline established during the work done in masters thesis. Doing research on Continual Learning for generalisation of AI algorithms for EMG signal processing.
Working Student - Forschungszentrum Jülich (PGI15 - Neuromorphic Software Ecosystems)
Developing multimodal acquisition systems with EMG sensors, event-based cameras, and IMUs. Creating multi-model datasets and training Deep Learning algorithms for computer vision and signal processing tasks.
M.Sc. Biomedical Engineering - Anhalt University
Focused on ICU Patient Care Equipment, Medical Technology, Biomedical Computation, Physiological Signal Analysis, and Medical Image Processing. Working on advanced medical device development and AI applications in healthcare.
Engineering Intern - Shahidzadeh Hospital
Supported yearly QA testing, calibration, and troubleshooting of critical care medical equipment. Documented QA processes and ensured compliance with operational and safety protocols. Assisted in device setup and technical support for hospital teams.
Skills & Expertise
Technical skillset spanning machine learning, signal processing, medical technology, and software development.
ML / Data Science
PyTorch, TensorFlow, Scikit-learn, MNE-Python, NumPy, Pandas. Experience with Transformers, LLMs, and training AI algorithms on custom datasets.
Programming
Python (Advanced), MATLAB (Proficient), C/C++ (Basic). Git, GitHub, Bash, Linux, OOP, Software Design Patterns, UML.
Signal Processing
EEG signal analysis, feature extraction using MNE-Python, NumPy, and SciPy. Physiological signal analysis and medical data processing.
Medical Technology
Clinical Information Systems, FHIR, MDR, FMEA, CAPA. Quality Management (ISO 13485, ISO 14971, ISO 10993), FDA 21 CFR.
Tools & Platforms
Unity (VR/AR), SolidWorks (CAD/CAM), SQL, Arduino. Computer vision with event-based cameras, EMG sensors, IMU integration.
Languages
English (C1), German (B2), Persian (Native). Strong communication skills for international research collaboration.
Selected Projects
Academic and personal projects demonstrating ML, medical technology, and software development skills.
Medical Device Risk Management
Designed comprehensive compliance and risk documentation for exoskeleton design in line with ISO 13485 and MDR standards, ensuring regulatory compliance.
EEG Signal Analysis
Recorded EEG data and applied advanced signal analysis and feature extraction using MNE-Python, NumPy, and SciPy for brain-computer interface applications.
Skin Cancer Detection
Trained a deep learning model in Python for dermatological screening using classification on open datasets, complete with an intuitive GUI.
Kitchenware Classification
Created an RGB image dataset from kitchenware and developed an ML model for real-time object classification with computer vision techniques.
VR Shooting Range Game
Developing a shooting range game in Unity for virtual reality glasses, exploring AR/VR development and interactive 3D environments.
EEG Classification (B.Sc. Thesis)
Implemented EEG classification methods on BCI competition dataset in MATLAB, exploring various signal processing and classification techniques.
Curriculum Vitae
Download my complete CV for detailed information about my education, research experience, publications, and technical expertise.
Download CV (PDF)