Welcome to my GitHub repository! I'm Yassmin Sayed, a Senior Biomedical Engineering student at Cairo University with a passion for leveraging AI, machine learning, and biomedical innovation to improve human health and well-being. Here, you'll find a showcase of my projects, research interests, and technical expertise, developed through academic work, internships, and hands-on experience.
I am focused on bridging the gap between healthcare challenges and technological solutions through interdisciplinary projects and research. With a GPA of 3.7, I expect to graduate in July 2025. My experience spans medical device innovation, AI-driven healthcare solutions, EEG signal processing, mental health diagnostics, and embedded systems. I am also an Egyptian Young Ambassador at NASA and have been involved in public speaking and volunteering to empower younger generations in STEM.
- Cairo University, Biomedical Engineering (Expected Graduation: July 2025)
- Internships:
- Biomedical Engineering Intern at Siemens Healthineers (MRI Systems)
- NASA Space Center Houston's Human Performance Accelerator Lab (Astronaut Training)
- Maintenance Engineering Intern at Arabic Engineering Office (MRI, ultrasound, and CT systems)
- Current Role: Data analysis intern at DEPI & AI Intern at Corelia.ai
- Objective: Develop a remote healthcare monitoring system integrating devices like blood pressure monitors, glucose meters, ECGs, and pulse oximeters.
- AI Integration: Uses machine learning models to predict emergency cases and improve mental health diagnostics through signal analysis and voice recordings.
- Goal: Enhance personalized health management, focusing on mental illness diagnostics with DSM-5 screening and clinical data integration.
- Dataset: IQ-OTH/NCCD lung cancer dataset from Kaggle
- Model: Developed a Convolutional Neural Network (CNN) for classifying lung cancer types (benign, malignant, normal).
- Focus: Model optimization and enhancing prediction accuracy for better cancer diagnostics.
- Description: A machine learning tool to predict disease progression using patient data and medical history.
- Outcome: Improved early diagnosis and personalized treatment planning.
- Objective: Process EEG signals to classify mental states and assist in mental health diagnostics.
- Tech Stack: Python, MATLAB
- Impact: Enhanced insights into mental state patterns, providing potential support for mental health evaluations.
- Description: A secure access system using voice recognition for authentication.
- Tech Stack: Embedded C, Signal Processing
- Focus: Developed a robust toolkit for face recognition using computer vision techniques.
- Applications: Enhancing security systems and user authentication processes.
- Machine Learning & Deep Learning for healthcare applications
- Medical Image Analysis: MRI, CT imaging, brain tumor diagnostics
- Remote Monitoring Systems: Integrating AI for emergency prediction in healthcare
- Mental Health Diagnostics: Using clinical vital signals (EEG, ECG, blood pressure, heart rate, temperature) for improved diagnosis
- Astronaut Health Research: Inspired by NASA experience, with a focus on bone health in space environments
- Autonomous Vehicles & Cybersecurity Resilience in cyber-physical systems
I've been a speaker and volunteer at events like NASA Space Apps Cairo and Cairo University workshops, sharing my journey in biomedical engineering and space health research. I aim to contribute to academia with future publications and insights from ongoing research.
- LinkedIn: Yassmin Sayed's Profile
- Email: yassmen.sayed1212@gmail.com
Thank you for visiting my GitHub portfolio! Feel free to explore the projects, and don't hesitate to reach out if you're interested in collaborating or discussing shared research interests.