Research, commentary, and practical guides on data science, machine learning, and analytics — from a practitioner working at the intersection of data, security, and AI in enterprise environments.
From raw data to production ML — the full stack of skills applied across research and enterprise environments.
Practical articles on data science, careers, and the analytical thinking behind modern technology decisions.
Published academic research with 700+ citations across cybersecurity, AI, and enterprise technology.
My interest in data science comes from necessity — in enterprise security and cloud infrastructure, decisions that aren't data-driven are just opinions. Good analytics distinguishes signal from noise, and that distinction matters enormously when you're defending healthcare systems or designing resilient infrastructure.
💡 "In God we trust. All others must bring data." — The governing philosophy behind every architectural decision and security strategy.
My published research applies quantitative methods to real-world problems: how ML improves threat detection accuracy, how DevOps pipeline data reveals security posture, and how IoT telemetry can be processed at scale for anomaly identification.
Beyond research, I mentor students building their first data portfolios and speak on the practical skills gap between academic data science training and enterprise expectations.
Research collaboration, speaking opportunities, or career conversations — reach out on LinkedIn or explore my published work.