I'm an Electrical Engineer passionate about bridging the gap between engineering and technology. My expertise spans across Machine Learning, DevOps, and Data Science, with a special focus on applying AI solutions to real-world problems, particularly in healthcare and infrastructure automation.
- π¬ Research Focus: Medical Imaging, Computer Vision, and Deep Learning
- βοΈ Cloud & DevOps: Azure, Kubernetes, AWS FinOps, Infrastructure as Code
- π Data Science: Advanced analytics, visualization, and predictive modeling
- π― Current Interest: MLOps, Model Context Protocol (MCP), and scalable ML infrastructure
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Breast Cancer Metastasis Classification β 5 stars
- Multi-stage cancer classification using Recursive Feature Elimination
- Advanced deep learning techniques for medical diagnosis
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Retinal OCT Image Classification
- CNN and Transfer Learning for retinal disease detection
- Computer vision applied to ophthalmology
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- Deep learning for COVID-19 detection using CT scans
- Contributing to pandemic response through AI
- Stock Market Forecasting
- 7-year analysis of FAANG stocks (META, AMZN, AAPL, NFLX, GOOG)
- Advanced time series forecasting with deep learning
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- Complete monitoring solution for Linux servers and MSSQL
- Infrastructure observability and alerting
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- MLOps infrastructure and deployment strategies
- Bridging ML and production environments
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- Cost optimization and financial operations automation
- Modern cloud cost management solutions
- π 112 Repositories: Extensive portfolio covering ML, DevOps, and data science
- β 8 GitHub Stars: Recognition from the developer community
- π₯ Active Contributor: 50+ contributions in the last year
- π Research Publications: Available on Google Scholar
- π MLOps: Building scalable machine learning infrastructure
- π€ Model Context Protocol (MCP): Next-generation AI model interaction
- βοΈ Cloud Cost Optimization: FinOps practices and automation
- π Advanced Analytics: Real-time data processing and visualization
- Advanced Kubernetes patterns for ML workloads
- Modern DevOps practices and CI/CD pipelines
- Cloud-native architecture and microservices
- Latest developments in AI/ML research
I'm always interested in collaborating on innovative projects, especially those involving:
- π₯ Healthcare AI: Medical imaging and diagnostic systems
- π MLOps: Scalable machine learning infrastructure
- βοΈ Cloud Solutions: DevOps and infrastructure automation
- π Data Analytics: Business intelligence and visualization
"Engineering the future through intelligent automation and data-driven insights"