Software Engineer | AI passionate | Biomedical Innovator

I am a full-stack software engineer and deep/machine learning engineer with a passion for leveraging technology to solve impactful real-world problems, especially in the healthcare domain. With a background in biomedical software engineering, clinical engineering, and over 5 years of experience in AI-driven solutions and SaaS developing. I have developed projects that range from health monitoring systems to advanced computer vision models.

I am always exploring new technologies and methodologies, from deep learning and computer vision to cloud computing and DevOps. My GitHub repositories reflect this versatility, showcasing my journey of continuous learning and my drive to make a positive impact.

• 🔬 Areas of Expertise: Full-Stack Development, Saas development, Software Architect, Machine Learning, Computer Vision, AI for Healthcare, Cloud Architecture.
• 📚 Languages: Python, Java, JavaScript, C++, and more.
• 💡 Tech Stack: Django, Angular, Ionic, Flutter, TensorFlow, PyTorch, React, Node.js, Docker, Kubernetes, and more.

A teleassistance system that monitors elderly people using smart home sensors. This system gathers thousands of daily data points and provides real-time analytics and alerts, ensuring safety and care for the elderly living independently.

• Tech Stack: Django, Angular, Python, Bash, MQTT, IoT, Domotics, Tensorflow, SciKit Learn.
• Key Features: Real-time monitoring, anomaly detection using ML models, custom notification system.
• Impact: Installed in over 30 households, improving quality of life for seniors and providing peace of mind to their families.

A machine learning-based solution using computer vision to identify cancer cells in a controlled blood flow environment. The system leverages advanced neural network models to classify cells at a rate of ~100 cells/second.

• Tech Stack: Python, TensorFlow, OpenCV, SciPy.
• Key Achievements: High classification accuracy in real-time scenarios, demonstrating potential for real-world clinical applications.

Developed a computer vision system for detecting skin carcinoma with a detection accuracy of ~97%. This project uses convolutional neural networks and data augmentation techniques to enhance model performance.

• Tech Stack: Python, TensorFlow, OpenCV, SciPy.
• Key Achievements: Demonstrated high diagnostic performance, contributing to AI-driven dermatological solutions.

A real-time system for non-invasive ocular tracking using infrared camera filters and CNNs. The system can determine ocular fixation points and measure pupil movement relative to the head, achieving 99% accuracy with sub-10ms inference time.

• Tech Stack: Python, TensorFlow, OpenCV.
• Key Achievements: High precision with an average error of <1 pixel in detecting pupil center; ideal for real-time applications.

• 💼 LinkedIn
• 🌐 Personal Website
• ✉️ Email: alessandropellegrino.dev@gmail.com
• 🔬 LeetCode

Thanks for stopping by! Feel free to explore my repositories, contribute, or reach out to discuss potential collaborations. Let’s build something impactful together! 🚀