About Me

I am a Privacy-Preserving Machine Learning Researcher with a PhD focused on federated learning for smart systems. My work sits at the intersection of machine learning, privacy-aware distributed systems, and rigorous experimental design.

I am particularly interested in building research that combines theoretical relevance, practical deployment value, and clean engineering execution. Alongside research contributions, I care deeply about reproducibility, strong infrastructure, and technical clarity.

My broader interests include federated learning, trustworthy AI, robust ML experimentation, and research-grade implementation for real-world intelligent systems.