Building AI systems that are intelligent, trustworthy, and deployable.

I design and implement AI-powered software systems that combine machine learning, production-grade engineering, and privacy-aware data architectures.

My work sits at the intersection of AI engineering, software development, and research, with a particular focus on privacy-preserving AI, trustworthy machine learning, healthcare data, and secure data-driven systems.

I build solutions across the full development lifecycle — from data pipelines, ML models, and AI APIs to interactive analytical platforms and deployable applications. My experience includes Python, FastAPI, PostgreSQL, PyTorch, TensorFlow, Scikit-learn, R, and modern data visualization technologies.

As a researcher and project lead, I translate research into practical systems. Through projects such as InviseeAI and ShinyAnonymizer, I have worked on real-world challenges involving healthcare data anonymization, privacy protection, compliance, and responsible AI deployment.

My goal is to build AI systems that are not only intelligent, but also reliable, explainable, secure, and practical to deploy.