ML Engineer | Medical Imaging & Privacy-Preserving Systems

Data Science student at FAU specializing in machine learning for healthcare and privacy-preserving technologies. I build production-ready AI systems with a focus on medical imaging and data engineering.

Currently working on:

• 🏥 Medical Imaging AI & segmentation models
• 🔒 Privacy-Preserving ML & EHR record linkage
• 🧠 Deep Learning (U-Net, CNNs, RNNs)
• 📊 ETL pipelines & data engineering
• 🚀 Production ML deployment

ML & Deep Learning:

Data Science:

Web & Deployment:

Full-stack semantic segmentation using TensorFlow & TensorFlow.js

• U-Net architecture for medical image segmentation
• Browser-based training with real-time inference
• 92% Dice Coefficient on test data
• Works on CPU & GPU browsers

Tech: TensorFlow, TensorFlow.js, React, Python, MONAI

End-to-end ETL for CO₂ emissions & rainfall analysis

• Automated data extraction & validation (Jayvee + Python)
• GitHub Actions CI/CD automation
• 99.2% data quality after cleaning
• Modern data engineering practices

Tech: Python, Jayvee, SQL, GitHub Actions, Pandas

Portfolio showcasing projects and achievements

MSc Data Science - Friedrich-Alexander-Universität Erlangen-Nürnberg (2023-Present)
BSc Computer Science & Engineering - IIUC Bangladesh (2018-2022)

Specialized Coursework:

• Deep Learning (CNNs, RNNs, Object Detection, Segmentation)
• Spatio-Temporal Data Analysis for Medical Imaging
• Advanced Statistics & High-Dimensional Data
• Data Engineering (ETL, Data Quality, CI/CD)

• 💼 ML Engineer roles in medical imaging & healthcare AI
• 🤝 Collaboration on healthcare AI projects
• 📚 Technical discussions & knowledge sharing
• 🔗 Networking in healthcare & AI community

Target Companies: Philips Healthineers, Siemens Healthineers, Carl Zeiss Meditec, Fraunhofer Institutes

If you find my projects interesting:

• ⭐ Star my repositories
• 🔔 Follow for updates on new projects
• 💬 Provide feedback on issues
• 🤝 Contribute to make projects better