A priori → a posteriori captures the essence of intelligent systems. It’s the point where what we think we know is challenged and refined by what we observe. This is how humans learn—as if the world is nothing but a complex Bayes’ network, continuously updating beliefs as new facts emerge.
This same principle of continuous learning has driven my entire career. I’m an AI/ML engineer in Cupertino, and over the past two decades, I’ve worked across application development, middleware, and cloud-native systems, ultimately focusing on AI.
Twenty years after my early work with neural networks and facial recognition, recent breakthroughs inspired me to return to academia. I earned my Master’s degree in Computer Science with a specialization in AI from Georgia Tech in 2024. Today, I partner with SF/Bay Area frontier labs and startups to build scalable GenAI, CoreML, and agentic systems that drive employee and developer productivity.
Outside of engineering, my world revolves around my son, Neiv, who is growing up faster than I can keep track of. I have historically stayed off social platforms, though I recently started sharing thoughts on Twitter/X. I also enjoy photography during my travels—some of which will eventually find its way here.