Muhammad Saad

I am a motivated and detail-oriented Biomedical Engineer with a strong foundation in AI/ML, experienced in building and deploying machine learning and deep learning models using Python, TensorFlow, PyTorch, and Scikit-learn. I have applied data preprocessing, model optimization, and statistical methods to extract insights and improve prediction accuracy on large datasets.

During an industry internship I developed and trained models, performed feature engineering and visualization, and deployed a real-time classification service using Flask and Streamlit. My projects include an ECG simulator for cloud-based signal recording and predictive analysis for early cardiac issue detection, and a whiteboard-cleaning automation system integrating computer vision, Arduino, and a user GUI.

I focus on scalable, production-ready solutions that bridge biomedical problems and AI, emphasizing reproducible workflows, model evaluation, and practical deployments that deliver measurable impact.