Machine Learning Engineer & AI Researcher
I explore the intersection of research and engineering in artificial intelligence. With training in statistics, algorithms, and machine learning, I design systems that are rigorous, scalable, and effective. My focus is on taking ideas from research to implementation — turning abstract concepts into working prototypes and real-world applications.
Time series forecasting with Prophet/ARIMA and inventory optimization logic. Implements advanced demand prediction algorithms with real-time stock management.
Product recommendations using association rules and collaborative filtering. Advanced ML algorithms for personalized fashion recommendations with real-time processing.
Customer clustering with KMeans and churn prediction with ML classifiers. Comprehensive customer analytics with predictive modeling for retention strategies.
Price elasticity modeling and revenue maximization via demand forecasting. Advanced optimization algorithms for dynamic pricing strategies in hospitality.
Real-time anomaly detection using Isolation Forest and One-Class SVM. Advanced streaming analytics for fraud detection and transaction monitoring.
Predictive modeling of sales vs spend and budget reallocation for ROI. Advanced ML models with SHAP explainability for marketing campaign optimization.
Workforce scheduling using linear programming and optimization under constraints. Advanced OR techniques for optimal staff allocation and scheduling efficiency.
Content generation for product descriptions, social posts, and emails with tone and language control. Advanced LLM integration with Hugging Face for automated marketing content creation.
Responsible AI framework for automating ML audits on AWS cloud infrastructure. Comprehensive compliance checking, bias detection, and risk assessment for production ML systems.
Intelligent multi-agent system for complex problem-solving and task automation. Orchestrates specialized AI agents for collaborative decision-making and workflow optimization.
A comprehensive guide to implementing automated compliance auditing for machine learning pipelines in cloud environments, focusing on fairness, transparency, and bias detection.
Read ArticleDoctoral research on automating legal and ethical compliance for trustworthy AI systems, supervised by Prof. Jean-Gabriel Ganascia and Dr. Gauvain Bourgne.
View ThesisResearch on integrating responsible AI values into automated decision-making systems for ethical compliance and transparency.
View on ScholarI'm always interested in discussing new opportunities, collaborations, and innovative projects in AI/ML and responsible technology.