Machine Learning Engineer and AI Researcher with expertise in machine learning, deep learning, and computer vision. Passionate about advancing AI research and developing adaptive systems for real-world applications. Strong background in data-driven modeling, algorithm optimization, and applied artificial intelligence.
Project Engineer
Highlights
Led the design and installation of security and alarm systems (Access Control, CCTV, Fire Alarm)
Contributed to database management improvements and project coordination.
Planned, supervised, and coordinated project board meetings.
Responded to tenders from clients.
Graduate Researcher
Highlights
Developed a novel cognitive architecture incorporating dynamic ensemble learning for real-time customer churn prediction.
Collaborated with interdisciplinary team on real-time prediction systems using the Cognitive Systems Toolkit (CST).
Implemented and evaluated six machine learning models (KNN, Logistic Regression, Decision Tree, Random Forest, SVM, Naive Bayes) in both individual and ensemble configurations.
Utilized Python, scikit-learn, and statistical analysis tools to validate results through Wilcoxon signed-rank testing
Data Scientist Intern
Highlights
Developed predictive models using machine learning algorithms
Created SQL queries for efficient data extraction and analy.
Implemented data mining techniques for market analysis
Master of Science
Computer Science
Grade: 3.77/4.0
Courses
Machine Learning and Pattern Recognition
Topics in Artificial Intelligence
Data Science and Visualization in Health
Topics in Machine Learning
Graph Algorithms
Scientific Methodology for Computing
Bachelor of Science
Pure and Applied Physics
Native speaker
Good working knowledge
→
Summary
This project demonstrates the construction of an end-to-end machine learning pipeline utilizing DVC (Data Version Control) for data and model versioning, MLflow for experiment tracking, and DagsHub as the central platform for repository management.